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: "estimating the number of communities in a network"
  Abstract: Community detection, the division of a network into dense subnetworks with only sparse connections between them, has been a topic of vigorous study in recent years. However, while there exist a range of effective methods for dividing a network into a specified number of communities, it is an open question how to determine exactly how many communities one should use. Here we describe a mathematically principled approach for finding the number of communities in a network by maximizing the integrated likelihood of the observed network structure under an appropriate generative model. We demonstrate the approach on a range of benchmark networks, both real and computer generated.

Connected through hyperedge HE-1 (induced by source paper "mean field theory of graph neural networks in graph partitioning"):
  with members:
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "algorithmic detectability threshold of the stochastic block model"
    - "deriving neural architectures from sequence and graph kernels"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "neural message passing for quantum chemistry"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-2 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"

Connected through hyperedge HE-3 (induced by source paper "democratic summary of public opinions in free response surveys"):
  with members:
    - "network structure from rich but noisy data"
    - "comparative analysis on the selection of number of clusters in community detection"
    - "reconstructing networks with unknown and heterogeneous errors"
    - "algorithmic detectability threshold of the stochastic block model"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "algorithmic infeasibility of community detection in higher order networks"
    - "cross validation estimate of the number of clusters in a network"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-4 (induced by source paper "bayesian estimation of the latent dimension and communities in stochastic blockmodels"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "exact recovery in the stochastic block model"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "clustering and community detection in directed networks a survey"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-5 (induced by source paper "adapting stochastic block models to power law degree distributions"):
  with members:
    - "role discovery in networks"
    - "model selection for degree corrected block models"
    - "dynamic infinite mixed membership stochastic blockmodel"
    - "the nonparametric metadata dependent relational model"
    - "oriented and degree generated block models generating and inferring communities with inhomogeneous degree distributions"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-6 (induced by source paper "comparative analysis on the selection of number of clusters in community detection"):
  with members:
    - "cross validation estimate of the number of clusters in a network"
    - "the ground truth about metadata and community detection in networks"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "localized eigenvectors of the non backtracking matrix"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "spectra of random graphs with community structure and arbitrary degrees"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-7 (induced by source paper "searching for a single community in a graph"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "exact recovery in the stochastic block model"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "breaking the small cluster barrier of graph clustering"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-8 (induced by source paper "algorithmic detectability threshold of the stochastic block model"):
  with members:
    - "detectability thresholds of general modular graphs"
    - "structure and inference in annotated networks"
    - "identification of core periphery structure in networks"
    - "detectability of the spectral method for sparse graph partitioning"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse 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)