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: "measuring and modeling bipartite graphs with community structure"
  Abstract: Network science is a powerful tool for analyzing complex systems in fields ranging from sociology to engineering to biology. This paper is focused on generative models of large-scale bipartite graphs, also known as two-way graphs or two-mode networks. We propose two generative models that can be easily tuned to reproduce the characteristics of real-world networks, not just qualitatively, but quantitatively. The characteristics we consider are the degree distributions and the metamorphosis coefficient. The metamorphosis coefficient, a bipartite analogue of the clustering coefficient, is the proportion of length-three paths that participate in length-four cycles. Having a high metamorphosis coefficient is a necessary condition for close-knit community structure. We define edge, node, and degreewise metamorphosis coefficients, enabling a more detailed understanding of the bipartite connectivity that is not explained by degree distribution alone. Our first model, bipartite Chung-Lu (CL), is able to reproduce real-world degree distributions, and our second model, bipartite block two-level Erd\"os-R\'enyi (BTER), reproduces both the degree distributions as well as the degreewise metamorphosis coefficients. We demonstrate the effectiveness of these models on several real-world data sets.

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 "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"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "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"
    - "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"
    - "graphlet decomposition framework algorithms and applications"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-4 (induced by source paper "balance in signed bipartite networks"):
  with members:
    - "butterfly counting in bipartite networks"
    - "signed network modeling based on structural balance theory"
    - "sne signed network embedding"
    - "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-5 (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"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "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-6 (induced by source paper "efficient butterfly counting for large bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "butterfly counting in bipartite networks"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "compressing graphs and indexes with recursive graph bisection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "vertex priority based butterfly counting for large scale bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "peeling bipartite networks for dense subgraph discovery"
    - "compressing graphs and indexes with recursive graph bisection"
    - "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-8 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples"):
  with members:
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"

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)