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: "peeling bipartite networks for dense subgraph discovery"
  Abstract: Finding dense bipartite subgraphs and detecting the relations among them is an important problem for affiliation networks that arise in a range of domains, such as social network analysis, word-document clustering, the science of science, internet advertising, and bioinformatics. However, most dense subgraph discovery algorithms are designed for classic, unipartite graphs. Subsequently, studies on affiliation networks are conducted on the co-occurrence graphs (e.g., co-author and co-purchase) that project the bipartite structure to a unipartite structure by connecting two entities if they share an affiliation. Despite their convenience, co-occurrence networks come at a cost of loss of information and an explosion in graph sizes, which limit the quality and the efficiency of solutions. We study the dense subgraph discovery problem on bipartite graphs. We define a framework of bipartite subgraphs based on the butterfly motif (2,2-biclique) to model the dense regions in a hierarchical structure. We introduce efficient peeling algorithms to find the dense subgraphs and build relations among them. We can identify denser structures compared to the state-of-the-art algorithms on co-occurrence graphs in real-world data. Our analyses on an author-paper network and a user-product network yield interesting subgraphs and hierarchical relations such as the groups of collaborators in the same institution and spammers that give fake ratings.

Connected through hyperedge HE-1 (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"
    - "butterfly counting in bipartite networks"
    - "generalized two mode cores"
    - "truss decomposition in massive networks"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-2 (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"
    - "butterfly counting in bipartite networks"
    - "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
    - "on sampling from massive graph streams"
    - "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-3 (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"
    - "butterfly counting in bipartite networks"
    - "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-4 (induced by source paper "counting butterfies from a large bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "on sampling from massive graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "evolution of directed triangle motifs in the google osn"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triangle counting in dynamic graph streams"
    - "parallel triangle counting in massive streaming graphs"

Connected through hyperedge HE-5 (induced by source paper "vertex priority based butterfly counting for large scale bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "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"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-6 (induced by source paper "butterfly counting in bipartite networks"):
  with members:
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "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"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
    - "triadic measures on graphs the power of wedge sampling"

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.SI(Social and Information Networks)