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 "vertex priority based butterfly counting for large scale bipartite networks"):
  Abstract: Bipartite networks are of great importance in many real-world applications. In bipartite networks, butterfly (i.e., a complete 2 x 2 biclique) is the smallest non-trivial cohesive structure and plays a key role. In this paper, we study the problem of efficiently counting the number of butterflies in a bipartite network. This problem has been recently studied. The most efficient existing techniques are based on enumerating wedges which is the dominant cost of counting butterflies. Nevertheless, the existing algorithms can hardly handle large-scale bipartite networks. This becomes a bottleneck in large-scale applications. In this paper, instead of the existing layer-priority-based techniques, we propose a vertex-priority-based paradigm BFC-VP to enumerate much fewer wedges; this leads to a significant improvement of the time complexity of the state-of-the-art algorithm. Moreover, we also present cache-aware strategies to further improve the time efficiency while theoretically retaining the time complexity of BFC-VP. These not only resolve the issue that the existing techniques cannot finish on some real datasets but also extensive empirical studies demonstrate that our techniques can speed up the state-of-the-art techniques by up to two orders of magnitude for the real datasets when the existing techniques can finish.

Includes members:
  Member "a survey of community search over big graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "peeling bipartite networks for dense subgraph discovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs")
  Member "compressing graphs and indexes with recursive graph bisection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "techniques for inverted index compression")
  Member "measuring and modeling bipartite graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "when engagement meets similarity efficient k r core computation on social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of community search over big graphs")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the power of pivoting for exact clique counting")
  Member "triadic measures on graphs the power of wedge sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "efficient triangle counting in large graphs via degree based vertex partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

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.SI(Social and Information Networks)