human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "counting triangles in large graphs on gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")
  Member "counting triangles in massive graphs with mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "truss decomposition in massive networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs")
  Member "efficient subgraph matching on billion node graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast detection of maximum common subgraph via deep q learning")

Question: Please tell me which class the source paper 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)