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 "community detection through likelihood optimization in search of a sound model"):
  Abstract: Community detection has recently became one of the most important problems in network analysis and many algorithms have been proposed to solve this problem. Among others, methods based on statistical inference are of particular interest: such algorithms are mathematically sound and were shown to provide partitions of good quality. The main ingredient of statistical inference methods is to assume some underlying random graph model and then fit this model to the observed graph by maximizing the likelihood. The choice of this underlying model (a.k.a. null model) is very important for such methods and this choice is the focus of the current study. We provide an extensive theoretical and empirical analysis to compare several models: the stochastic block model, which is currently the most widely used model for likelihood optimization; recently proposed degree-corrected modification of this model, and a new one-parametric null model which has some desirable statistical properties. We also develop and compare two likelihood optimization algorithms suitable for the null models under consideration. An extensive empirical analysis on a variety of datasets shows, in particular, that, in contrast to synthetic datasets, real-world networks have diverse nature and there is no model suitable for all of them.

Includes members:
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "equivalence between modularity optimization and maximum likelihood methods for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mga momentum gradient attack on network")
  Member "metrics for community analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when less is more systematic analysis of cascade based community detection")
  Member "think locally act locally detection of small medium sized and large communities in large networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "network structure exploration via bayesian nonparametric models":
    also connected through other hyperedges:
      (none)
  Member "clustering and community detection in directed networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of community search over big graphs")
  Member "model of complex networks based on citation dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low dimensional statistical manifold embedding of directed graphs")

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.SI(Social and Information Networks)