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: "model selection for degree corrected block models"
  Abstract: The proliferation of models for networks raises challenging problems of model selection: the data are sparse and globally dependent, and models are typically high-dimensional and have large numbers of latent variables. Together, these issues mean that the usual model-selection criteria do not work properly for networks. We illustrate these challenges, and show one way to resolve them, by considering the key network-analysis problem of dividing a graph into communities or blocks of nodes with homogeneous patterns of links to the rest of the network. The standard tool for undertaking this is the stochastic block model, under which the probability of a link between two nodes is a function solely of the blocks to which they belong. This imposes a homogeneous degree distribution within each block; this can be unrealistic, so degree-corrected block models add a parameter for each node, modulating its overall degree. The choice between ordinary and degree-corrected block models matters because they make very different inferences about communities. We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs. We also develop linear-time approximations for log-likelihoods under both the stochastic block model and the degree-corrected model, using belief propagation. Applications to simulated and real networks show excellent agreement with our approximations. Our results thus both solve the practical problem of deciding on degree correction and point to a general approach to model selection in network analysis.

Connected through hyperedge HE-1 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "community discovery in dynamic networks a survey"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "random graph models for dynamic networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"

Connected through hyperedge HE-2 (induced by source paper "adapting stochastic block models to power law degree distributions"):
  with members:
    - "estimating the number of communities in a network"
    - "role discovery in networks"
    - "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-3 (induced by source paper "structure of online dating markets in us cities"):
  with members:
    - "aspirational pursuit of mates in online dating markets"

Connected through hyperedge HE-4 (induced by source paper "generative models for local network community detection"):
  with members:
    - "community detection in networks with unequal groups"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-5 (induced by source paper "non linear attributed graph clustering by symmetric nmf with pu learning"):
  with members:
    - "structural rnn deep learning on spatio temporal graphs"
    - "pu learning for matrix completion"

Connected through hyperedge HE-6 (induced by source paper "modelling sequences and temporal networks with dynamic community structures"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "maps of sparse markov chains efficiently reveal community structure in network flows with memory"
    - "representing higher order dependencies in networks"
    - "dynamic stochastic blockmodels for time evolving social networks"
    - "detecting change points in the large scale structure of evolving networks"
    - "stochastic block transition models for dynamic networks"
    - "dynamic stochastic blockmodels statistical models for time evolving networks"

Connected through hyperedge HE-7 (induced by source paper "cross validation estimate of the number of clusters in a network"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "estimating the number of communities in a network"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "centrality metrics and localization in core periphery networks"):
  with members:
    - "identification of core periphery structure in networks"
    - "localization and centrality in networks"
    - "spectral properties of google matrix of wikipedia and other networks"
    - "spectral redemption in clustering sparse networks"
    - "graph spectra and the detectability of community structure in networks"
    - "core periphery structure in 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)