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: "the similarity between stochastic kronecker and chung lu graph models"
  Abstract: The analysis of massive graphs is now becoming a very important part of science and industrial research. This has led to the construction of a large variety of graph models, each with their own advantages. The Stochastic Kronecker Graph (SKG) model has been chosen by the Graph500 steering committee to create supercomputer benchmarks for graph algorithms. The major reasons for this are its easy parallelization and ability to mirror real data. Although SKG is easy to implement, there is little understanding of the properties and behavior of this model. #R##N#We show that the parallel variant of the edge-configuration model given by Chung and Lu (referred to as CL) is notably similar to the SKG model. The graph properties of an SKG are extremely close to those of a CL graph generated with the appropriate parameters. Indeed, the final probability matrix used by SKG is almost identical to that of a CL model. This implies that the graph distribution represented by SKG is almost the same as that given by a CL model. We also show that when it comes to fitting real data, CL performs as well as SKG based on empirical studies of graph properties. CL has the added benefit of a trivially simple fitting procedure and exactly matching the degree distribution. Our results suggest that users of the SKG model should consider the CL model because of its similar properties, simpler structure, and ability to fit a wider range of degree distributions. At the very least, CL is a good control model to compare against.

Connected through hyperedge HE-1 (induced by source paper "signed network modeling based on structural balance theory"):
  with members:
    - "learning deep generative models of graphs"
    - "signed graph convolutional network"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "community structure and scale free collections of erdős renyi graphs"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "asymptotic analysis of equivalences and core structures in kronecker style graph models"):
  with members:
    - "hyperbolicity degeneracy and expansion of random intersection graphs"
    - "core periphery structure in networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-3 (induced by source paper "the infinity mirror test for analyzing the robustness of graph generators"):
  with members:
    - "a scalable generative graph model with community structure"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"

Connected through hyperedge HE-4 (induced by source paper "fast generation of complex networks with underlying hyperbolic geometry"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "a scalable generative graph model with community structure"
    - "community structure and scale free collections of erdős renyi graphs"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-5 (induced by source paper "fast generation of dynamic complex networks with underlying hyperbolic geometry"):
  with members:
    - "a scalable generative graph model with community structure"
    - "community structure and scale free collections of erdős renyi graphs"
    - "sustaining the internet with hyperbolic mapping"

Connected through hyperedge HE-6 (induced by source paper "networkit a tool suite for large scale complex network analysis"):
  with members:
    - "querying probabilistic neighborhoods in spatial data sets efficiently"
    - "engineering parallel algorithms for community detection in massive networks"
    - "snap a general purpose network analysis and graph mining library"
    - "structure preserving sparsification methods for social networks"
    - "fully dynamic approximation of betweenness centrality"
    - "approximating betweenness centrality in large evolving networks"
    - "social structure of facebook networks"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-7 (induced by source paper "a scalable generative graph model with community structure"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "an in depth analysis of stochastic kronecker graphs"
    - "decompositions of triangle dense graphs"
    - "triadic measures on graphs the power of wedge sampling"
    - "community structure and scale free collections of erdős renyi graphs"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"
    - "revisiting degree distribution models for social graph analysis"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "networkit an interactive tool suite for high performance network analysis"):
  with members:
    - "approximating betweenness centrality in large evolving networks"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"
    - "an o m algorithm for cores decomposition of networks"

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)