Fix CUDSS documentation to correctly describe LUFactorization usage

- Clarified that CUDSS works through LUFactorization() when CUDSS.jl is loaded
- Explained that it automatically uses cuDSS for CuSparseMatrixCSR arrays
- Removed incorrect reference to a separate CUDSS factorization type

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

Author: ChrisRackauckas

docs/src/solvers/solvers.md

@@ -43,11 +43,12 @@ For sparse LU-factorizations, `KLUFactorization` if there is less structure
 to the sparsity pattern and `UMFPACKFactorization` if there is more structure.
 Pardiso.jl's methods are also known to be very efficient sparse linear solvers.
 
-For GPU-accelerated sparse LU-factorizations, both CUDSS.jl and CUSOLVERRF.jl provide
-high-performance options. CUDSS.jl offers a modern interface to NVIDIA's cuDSS library,
-while `CUSOLVERRFFactorization` provides access to NVIDIA's cusolverRF library. Both
-offer significant performance improvements for sparse systems on CUDA-capable GPUs and
-are particularly effective for large sparse matrices that can benefit from GPU parallelization.
+For GPU-accelerated sparse LU-factorizations, there are two high-performance options.
+When using CuSparseMatrixCSR arrays with CUDSS.jl loaded, `LUFactorization()` will
+automatically use NVIDIA's cuDSS library. Alternatively, `CUSOLVERRFFactorization`
+provides access to NVIDIA's cusolverRF library. Both offer significant performance
+improvements for sparse systems on CUDA-capable GPUs and are particularly effective
+for large sparse matrices that can benefit from GPU parallelization.
 
 While these sparse factorizations are based on implementations in other languages,
 and therefore constrained to standard number types (`Float64`,  `Float32` and