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: "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.

Connected through hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries"):
  with members:
    - "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"
    - "heterogeneous network motifs"
    - "edmot an edge enhancement approach for motif aware community detection"
    - "hypergraph clustering with categorical edge labels"
    - "motivo fast motif counting via succinct color coding and adaptive sampling"
    - "mining frequent patterns in evolving graphs"
    - "rept a streaming algorithm of approximating global and local triangle counts in parallel"
    - "simplicial closure and higher order link prediction"

Connected through hyperedge HE-2 (induced by source paper "efficient bitruss decomposition for large scale bipartite graphs"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "generalized two mode cores"
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-3 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"
    - "waddling random walk fast and accurate mining of motif statistics in large graphs"

Connected through hyperedge HE-4 (induced by source paper "fleet butterfly estimation from a bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "evolution of directed triangle motifs in the google osn"

Connected through hyperedge HE-5 (induced by source paper "parallel algorithms for butterfly computations"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-6 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "signed network modeling based on structural balance theory"
    - "sne signed network embedding"
    - "measuring and modeling bipartite graphs with community structure"
    - "brazilian congress structural balance analysis"
    - "a survey of signed network mining in social media"
    - "common neighbours and the local community paradigm for topological link prediction in bipartite networks"
    - "exploiting the structure of bipartite graphs for algebraic and spectral graph theory applications"
    - "the link prediction problem in bipartite networks"

Connected through hyperedge HE-7 (induced by source paper "efficient butterfly counting for large bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "compressing graphs and indexes with recursive graph bisection"
    - "measuring and modeling bipartite graphs with community structure"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triadic measures on graphs the power of wedge sampling"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

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.DM(Discrete Mathematics)