human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a gemm interface and implementation on nvidia gpus for multiple small matrices"
  Abstract: We present an interface and an implementation of the General Matrix Multiply (GEMM) routine for multiple small matrices processed simultaneously on NVIDIA graphics processing units (GPUs). We focus on matrix sizes under 16. The implementation can be easily extended to larger sizes. For single precision matrices, our implementation is 30% to 600% faster than the batched cuBLAS implementation distributed in the CUDA Toolkit 5.0 on NVIDIA Tesla K20c. For example, we obtain 104 GFlop/s and 216 GFlop/s when multiplying 100,000 independent matrix pairs of size 10 and 16, respectively. Similar improvement in performance is obtained for other sizes, in single and double precision for real and complex types, and when the number of matrices is smaller. Apart from our implementation, our different function interface also plays an important role in the improved performance. Applications of this software include Finite Element computation on GPUs.

Connected through hyperedge HE-1 (induced by source paper "simultaneous solving of batched linear programs on a gpu"):
  with members:
    - "on simplex pivoting rules and complexity theory"

Connected through hyperedge HE-2 (induced by source paper "deep roots improving cnn efficiency with hierarchical filter groups"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "compressing neural networks with the hashing trick"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "deep learning with limited numerical precision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "tensor contractions with extended blas kernels on cpu and gpu"):
  with members:
    - "on the performance prediction of blas based tensor contractions"
    - "fourier pca and robust tensor decomposition"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "performance modeling for dense linear algebra"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-4 (induced by source paper "solving batched linear programs on gpu and multicore cpu"):
  with members:
    - "high performance physics simulations using multi core cpus and gpgpus in a volunteer computing context"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.MS(Mathematical Software)