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 "butterfly counting in bipartite networks"):
  Abstract: Graph motifs are used to analyze networks from diverse domains. We consider the problem of counting motifs in bipartite affiliation networks, such as author-paper, user-product, and actor-movie relations. The substantial prior work on counting motifs in unipartite graphs, such as triangle counting, does not apply here since bipartite graphs do not have triangles. Unlike the solution of projecting bipartite graphs onto unipartite graphs, which leads to a substantial increase in the size of the network, we directly address counting motifs in bipartite networks. We focus on counting the number of occurrences of a \butterfly", a complete 2 \times 2 subgraph, which is also the simplest cohesive and bipartite higher-order structure. Our main contribution is a suite of randomized algorithms for approximating the number of butterflies in a graph with provable accuracy guarantees. An experimental evaluation on large real-world networks shows that our algorithms can return accurate estimates within a few seconds, even for networks with trillions of butterflies and hundreds of millions of edges.

Includes members:
  Member "edge based wedge sampling to estimate triangle counts in very large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "retrieving top weighted triangles in graphs")
  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 "escape efficiently counting all 5 vertex subgraphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "a fast and provable method for estimating clique counts using tur an s theorem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "measuring and modeling bipartite graphs with community structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "graphlet decomposition framework algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "path sampling a fast and provable method for estimating 4 vertex subgraph counts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the power of pivoting for exact clique counting")
  Member "wedge sampling for computing clustering coefficients and triangle counts on large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the power of pivoting for exact clique 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.DM(Discrete Mathematics)