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: "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.

Connected through hyperedge HE-1 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"
    - "submodular inference of diffusion networks from multiple trees"

Connected through hyperedge HE-2 (induced by source paper "publishing community preserving attributed social graphs with a differential privacy guarantee"):
  with members:
    - "robust active attacks on social graphs"
    - "using node centrality and optimal control to maximize information diffusion in social networks"
    - "detecting communities under differential privacy"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "a scalable generative graph model with community structure"
    - "community detection in networks with node attributes"
    - "differentially private data analysis of social networks via restricted sensitivity"
    - "preserving link privacy in social network based systems"

Connected through hyperedge HE-3 (induced by source paper "using synthetic networks for parameter tuning in community detection"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "clustering and community detection in directed networks a survey"
    - "model of complex networks based on citation dynamics"
    - "a classification for community discovery methods in complex networks"
    - "sustaining the internet with hyperbolic mapping"

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)