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: "novel graph processor architecture prototype system and results"
  Abstract: Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based graph instruction set, a cacheless memory system, accelerator-based architecture, a systolic sorter, high-bandwidth multidimensional toroidal communication network, and randomized communications. A field-programmable gate array (FPGA) prototype of the new graph processor has been developed with significant performance enhancement over conventional processors in graph computational throughput.

Connected through hyperedge HE-1 (induced by source paper "a survey on graph processing accelerators challenges and opportunities"):
  with members:
    - "flashabacus a self governing flash based accelerator for low power systems"
    - "relational inductive biases deep learning and graph networks"
    - "struc2vec learning node representations from structural identity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "democratizing design for future computing platforms"
    - "graph2vec learning distributed representations of graphs"
    - "graphr accelerating graph processing using reram"
    - "5g green cellular networks considering power allocation schemes"

Connected through hyperedge HE-2 (induced by source paper "redisgraph graphblas enabled graph database"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "static graph challenge subgraph isomorphism"
    - "mathematical foundations of the graphblas"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-3 (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"
    - "enabling massive deep neural networks with the graphblas"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "tabularosa tabular operating system architecture for massively parallel heterogeneous compute engines"):
  with members:
    - "scalable system scheduling for hpc and big data"
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-6 (induced by source paper "enabling massive deep neural networks with the graphblas"):
  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"
    - "mathematical foundations of the graphblas"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "approximating betweenness centrality in fully dynamic networks"
    - "gunrock a high performance graph processing library on the gpu"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

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)