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: "modeling deep learning accelerator enabled gpus"
  Abstract: The efficacy of deep learning has resulted in its use in a growing number of applications. The Volta graphics processor unit (GPU) architecture from NVIDIA introduced a specialized functional unit, the "tensor core", that helps meet the growing demand for higher performance for deep learning. In this paper we study the design of the tensor cores in NVIDIA's Volta and Turing architectures. We further propose an architectural model for the tensor cores in Volta. When implemented a GPU simulator, GPGPU-Sim, our tensor core model achieves 99.6\% correlation versus an NVIDIA Titan~V GPU in terms of average instructions per cycle when running tensor core enabled GEMM workloads. We also describe support added to enable GPGPU-Sim to run CUTLASS, an open-source CUDA C++ template library providing customizable GEMM templates that utilize tensor cores.

Connected through hyperedge HE-1 (induced by source paper "balancing efficiency and flexibility for dnn acceleration via temporal gpu systolic array integration"):
  with members:
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mask r cnn"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to track at 100 fps with deep regression networks"
    - "orb slam a versatile and accurate monocular slam system"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-2 (induced by source paper "accelerating reduction and scan using tensor core units"):
  with members:
    - "nvidia tensor core programmability performance precision"
    - "culda_cgs solving large scale lda problems on gpus"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scaling sgd batch size to 32k for imagenet training"
    - "mixed precision training"

Connected through hyperedge HE-3 (induced by source paper "a computational model for tensor core units"):
  with members:
    - "accelerating reduction and scan using tensor core units"
    - "nvidia tensor core programmability performance precision"
    - "mobile machine learning hardware at arm a systems on chip soc perspective"
    - "in datacenter performance analysis of a tensor processing unit"
    - "benchmarking state of the art deep learning software tools"
    - "identity mappings in deep residual networks"
    - "powers of tensors and fast matrix multiplication"

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)