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: "counting triangles in large graphs on gpu"
  Abstract: The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. We describe the implementation details necessary to achieve high performance and present the experimental evaluation of our approach. The algorithm achieves 15 to 35 times speedup over our CPU implementation, and is capable of finding 8.8 billion triangles in a 180 million edges graph in 12 seconds on the Nvidia GeForce GTX 980 GPU.

Connected through hyperedge HE-1 (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-2 (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"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "highly parallel sparse matrix matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "gunrock gpu graph analytics"):
  with members:
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "snap a general purpose network analysis and graph mining library"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a comparative study on exact triangle counting algorithms on the gpu"):
  with members:
    - "approximately counting triangles in sublinear time"
    - "gunrock a high performance graph processing library on the gpu"
    - "counting triangles in massive graphs with mapreduce"
    - "truss decomposition in massive networks"
    - "efficient subgraph matching on billion node graphs"

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)