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: "sparse deep neural network exact solutions"
  Abstract: Deep neural networks (DNNs) have emerged as key enablers of machine learning. Applying larger DNNs to more diverse applications is an important challenge. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN. As DNNs incorporate more layers and more neurons per layers, these weight matrices may be required to be sparse because of memory limitations. Sparse DNNs are one possible approach, but the underlying theory is in the early stages of development and presents a number of challenges, including determining the accuracy of inference and selecting nonzero weights for training. Associative array algebra has been developed by the big data community to combine and extend database, matrix, and graph/network concepts for use in large, sparse data problems. Applying this mathematics to DNNs simplifies the formulation of DNN mathematics and reveals that DNNs are linear over oscillating semirings. This work uses associative array DNNs to construct exact solutions and corresponding perturbation models to the rectified linear unit (ReLU) DNN equations that can be used to construct test vectors for sparse DNN implementations over various precisions. These solutions can be used for DNN verification, theoretical explorations of DNN properties, and a starting point for the challenge of sparse training.

Connected through hyperedge HE-1 (induced by source paper "sparse deep neural network graph challenge"):
  with members:
    - "radix net structured sparse matrices for deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "streaming graph challenge stochastic block partition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "static graph challenge subgraph isomorphism"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-2 (induced by source paper "radix net structured sparse matrices for deep neural networks"):
  with members:
    - "expressive power of recurrent neural networks"
    - "deep expander networks efficient deep networks from graph theory"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the expressive power of neural networks a view from the width"
    - "enabling massive deep neural networks with the graphblas"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural network topologies for sparse training"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enabling massive deep neural networks with the graphblas"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "data free parameter pruning for deep 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.LG(Machine Learning)