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 "comparative analysis on the selection of number of clusters in community detection"):
  Abstract: We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have, becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

Includes members:
  Member "cross validation estimate of the number of clusters in a network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "democratic summary of public opinions in free response surveys")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "estimating the number of communities in a network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mean field theory of graph neural networks in graph partitioning")
  Member "identification of core periphery structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "link prediction in networks with core fringe data")
  Member "detectability of the spectral method for sparse graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "localized eigenvectors of the non backtracking matrix":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "detectability of the spectral method for sparse graph partitioning")
  Member "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "spectra of random graphs with community structure and arbitrary degrees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "community detectability and structural balance dynamics in signed networks")

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)