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: "on the relationship between gaussian stochastic blockmodels and label propagation algorithms"
  Abstract: The problem of community detection has received great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of the label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks.

Connected through hyperedge HE-1 (induced by source paper "an exact no free lunch theorem for community detection"):
  with members:
    - "metrics matter in community detection"
    - "evaluation of community structures using kappa index and f score instead of normalized mutual information"
    - "the ground truth about metadata and community detection in networks"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "estimating network parameters for selecting community detection algorithms"

Connected through hyperedge HE-2 (induced by source paper "metrics matter in community detection"):
  with members:
    - "an exact no free lunch theorem for community detection"
    - "the ground truth about metadata and community detection in networks"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "label propagation for clustering"):
  with members:
    - "corrected overlap weight and clustering coefficient"
    - "the many facets of community detection in complex networks"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "identifying robust communities and multi community nodes by combining top down and bottom up approaches to clustering"
    - "group detection in complex networks an algorithm and comparison of the state of the art"
    - "labelrank a stabilized label propagation algorithm for community detection in networks"
    - "demon a local first discovery method for overlapping communities"
    - "towards linear time overlapping community detection in social networks"

Connected through hyperedge HE-4 (induced by source paper "role based label propagation algorithm for community detection"):
  with members:
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)