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: "a comparative study on exact triangle counting algorithms on the gpu"
  Abstract: We implement exact triangle counting in graphs on the GPU using three different methodologies: subgraph matching to a triangle pattern; programmable graph analytics, with a set-intersection approach; and a matrix formulation based on sparse matrix-matrix multiplies. All three deliver best-of-class performance over CPU implementations and over comparable GPU implementations, with the graph-analytic approach achieving the best performance due to its ability to exploit efficient filtering steps to remove unnecessary work and its high-performance set-intersection core.

Connected through hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication"):
  with members:
    - "high performance sparse matrix matrix products on intel knl and multicore architectures"
    - "sparse matrix matrix multiplication on multilevel memory architectures algorithms and experiments"
    - "multi threaded sparse matrix matrix multiplication for many core and gpu architectures"
    - "porting of the dbcsr library for sparse matrix matrix multiplications to intel xeon phi systems"
    - "static graph challenge subgraph isomorphism"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model"
    - "a survey of sparse matrix vector multiplication performance on large matrices"

Connected through hyperedge HE-2 (induced by source paper "fast gunrock subgraph matching gsm on gpus"):
  with members:
    - "gsi gpu friendly subgraph isomorphism"
    - "fast bfs based triangle counting on gpus"
    - "gunrock gpu graph analytics"

Connected through hyperedge HE-3 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-4 (induced by source paper "graphblast a high performance linear algebra based graph framework on the gpu"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "implementing push pull efficiently in graphblas"
    - "extreme scale de novo metagenome assembly"
    - "junction tree variational autoencoder for molecular graph generation"
    - "gunrock gpu graph analytics"
    - "mathematical foundations of the graphblas"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "a 2d parallel triangle counting algorithm for distributed memory architectures"):
  with members:
    - "distributed memory parallel algorithms for counting and listing triangles in big graphs"
    - "static graph challenge subgraph isomorphism"
    - "counting triangles in large graphs on gpu"
    - "highly parallel sparse matrix matrix multiplication"

Connected through hyperedge HE-6 (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-7 (induced by source paper "on large scale graph generation with validation of diverse triangle statistics at edges and vertices"):
  with members:
    - "design generation and validation of extreme scale power law graphs"
    - "distributed triangle counting in the graphulo matrix math library"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "directed closure measures for networks with reciprocity"

Connected through hyperedge HE-8 (induced by source paper "gunrock gpu graph analytics"):
  with members:
    - "snap a general purpose network analysis and graph mining library"
    - "counting triangles in large graphs on gpu"
    - "mathematical foundations of the graphblas"
    - "accelerating direction optimized breadth first search on hybrid architectures"
    - "multi gpu graph analytics"
    - "emptyheaded boolean algebra based graph processing"
    - "gunrock a high performance graph processing library on the gpu"
    - "efficient large scale graph processing on hybrid cpu and gpu systems"

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)