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: "gemmbench a framework for reproducible and collaborative benchmarking of matrix multiplication"
  Abstract: The generic matrix-matrix multiplication (GEMM) is arguably the most popular computational kernel of the 20th century. Yet, surprisingly, no common methodology for evaluating GEMM performance has been established over the many decades of using GEMM for comparing architectures, compilers and ninja-class programmers. #R##N#We introduce GEMMbench, a framework and methodology for evaluating performance of GEMM implementations. GEMMbench is implemented on top of Collective Knowledge (CK), a lightweight framework for reproducible and collaborative R&D in computer systems. Using CK allows the R&D community to crowdsource hand-written and compiler-generated GEMM implementations and to study their performance across multiple platforms, data sizes and data types. #R##N#Our initial implementation supports hand-written OpenCL kernels operating on matrices consisting of single- and double-precision floating-point values, and producing single or multiple output elements per work-item (via thread coarsening and vectorization).

Connected through hyperedge HE-1 (induced by source paper "cltorch a hardware agnostic backend for the torch deep neural network library based on opencl"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a performance comparison of sort and scan libraries for gpus"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "cltune a generic auto tuner for opencl kernels"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

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)