fix readme of autofis

Author: wangzhen38

README_CN.md

@@ -160,7 +160,7 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml #  静态图训
   |   排序   |                  [Fibinet](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fibinet/)                  |  -  |       ✓     |     ✓     | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [RecSys19][FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction]( https://arxiv.org/pdf/1905.09433.pdf)                                                 |
   |   排序   |                     [FLEN](models/rank/flen/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf)                                                                                                           |
   |   排序   |                     [DeepRec](models/rank/deeprec/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [2017][Training Deep AutoEncoders for Collaborative Filtering](https://arxiv.org/pdf/1708.01715v3.pdf)                                                                                                          |
-  |   排序   |                     [AutoFIS](models/rank/autofis/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf)                                                                                                          |                    |  -  |       ✓     |     ✓     | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [2019][FLEN: Leveraging Field for Scalable CTR Prediction]( https://arxiv.org/pdf/1911.04690.pdf)                                                                                                           |
+  |   排序   |                     [AutoFIS](models/rank/autofis/)                     |  -  |       ✓     |     ✓     | >=2.1.0 | [KDD 2020][AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction](https://arxiv.org/pdf/2003.11235v3.pdf)
   |  多任务  |                  [PLE](models/multitask/ple/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/ple.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238938)  |       ✓     |     ✓     |  >=2.1.0 | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/abs/10.1145/3383313.3412236)                                                              |
   |  多任务  |                  [ESMM](models/multitask/esmm/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/esmm.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238583)  |       ✓     |     ✓     | >=2.1.0 | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                                                              |
   |  多任务  |                  [MMOE](models/multitask/mmoe/)([文档](https://paddlerec.readthedocs.io/en/latest/models/multitask/mmoe.html))                   |  [Python CPU/GPU](https://aistudio.baidu.com/aistudio/projectdetail/3238934)  |       ✓     |     ✓     | >=2.1.0 | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                                                       |