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: "enabling massive deep neural networks with the graphblas"
  Abstract: Deep Neural Networks (DNNs) have emerged as a core tool for machine learning. The computations performed during DNN training and inference are dominated by operations on the weight matrices describing the DNN. As DNNs incorporate more stages and more nodes per stage, these weight matrices may be required to be sparse because of memory limitations. The GraphBLAS.org math library standard was developed to provide high performance manipulation of sparse weight matrices and input/output vectors. For sufficiently sparse matrices, a sparse matrix library requires significantly less memory than the corresponding dense matrix implementation. This paper provides a brief description of the mathematics underlying the GraphBLAS. In addition, the equations of a typical DNN are rewritten in a form designed to use the GraphBLAS. An implementation of the DNN is given using a preliminary GraphBLAS C library. The performance of the GraphBLAS implementation is measured relative to a standard dense linear algebra library implementation. For various sizes of DNN weight matrices, it is shown that the GraphBLAS sparse implementation outperforms a BLAS dense implementation as the weight matrix becomes sparser.

Connected through hyperedge HE-1 (induced by source paper "ai data wrangling with associative arrays"):
  with members:
    - "polystore mathematics of relational algebra"
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "mathematical foundations of the graphblas"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-2 (induced by source paper "sparse deep neural network graph challenge"):
  with members:
    - "radix net structured sparse matrices for deep neural networks"
    - "sparse deep neural network exact solutions"
    - "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"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-3 (induced by source paper "streaming 1 9 billion hypersparse network updates per second with d4m"):
  with members:
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "streaming graph challenge stochastic block partition"
    - "static graph challenge subgraph isomorphism"
    - "polystore mathematics of relational algebra"
    - "associative array model of sql nosql and newsql databases"
    - "benchmarking scidb data import on hpc systems"
    - "lustre hadoop accumulo"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

Connected through hyperedge HE-4 (induced by source paper "radix net structured sparse matrices for deep neural networks"):
  with members:
    - "sparse deep neural network exact solutions"
    - "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"
    - "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-5 (induced by source paper "training behavior of sparse neural network topologies"):
  with members:
    - "graphchallenge org raising the bar on graph analytic performance"
    - "deep expander networks efficient deep networks from graph theory"
    - "exploring sparsity in recurrent neural networks"
    - "dynamic network surgery for efficient dnns"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "design generation and validation of extreme scale power law graphs"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "constructing adjacency arrays from incidence arrays"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-7 (induced by source paper "graphchallenge org raising the bar on graph analytic performance"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-8 (induced by source paper "sparse deep neural network exact solutions"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "polystore mathematics of relational algebra"
    - "theory ii landscape of the empirical risk in deep learning"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fast algorithms for convolutional neural networks"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"

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.DC(Distributed, Parallel, and Cluster Computing)