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: "pseudo likelihood methods for community detection in large sparse networks"
  Abstract: Many algorithms have been proposed for fitting network models with communities, but most of them do not scale well to large networks, and often fail on sparse networks. Here we propose a new fast pseudo-likelihood method for fitting the stochastic block model for networks, as well as a variant that allows for an arbitrary degree distribution by conditioning on degrees. We show that the algorithms perform well under a range of settings, including on very sparse networks, and illustrate on the example of a network of political blogs. We also propose spectral clustering with perturbations, a method of independent interest, which works well on sparse networks where regular spectral clustering fails, and use it to provide an initial value for pseudo-likelihood. We prove that pseudo-likelihood provides consistent estimates of the communities under a mild condition on the starting value, for the case of a block model with two communities.

Connected through hyperedge HE-1 (induced by source paper "power up robust graph convolutional network against evasion attacks based on graph powering"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "adversarial attacks on graph neural networks via meta learning"
    - "a comprehensive survey on graph neural networks"
    - "can adversarial network attack be defended"
    - "graph powering and spectral robustness"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adversarial attack on graph structured data"

Connected through hyperedge HE-2 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk"):
  with members:
    - "multi relational poincar e graph embeddings"
    - "representation learning for attributed multiplex heterogeneous network"
    - "mvn2vec preservation and collaboration in multi view network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "principled multilayer network embedding"
    - "a survey on network embedding"
    - "poincar e embeddings for learning hierarchical representations"
    - "representation learning for scale free networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "change detection in noisy dynamic networks a spectral embedding approach"):
  with members:
    - "change detection in dynamic attributed networks"
    - "a probabilistic framework to node level anomaly detection in communication networks"
    - "dyngem deep embedding method for dynamic graphs"
    - "link prediction for interdisciplinary collaboration via co authorship network"
    - "hellrank a hellinger based centrality measure for bipartite social networks"
    - "fast change point detection on dynamic social networks"
    - "detection and localization of change points in temporal networks with the aid of stochastic block models"
    - "localization and centrality in networks"

Connected through hyperedge HE-5 (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"
    - "estimating the number of communities in a network"
    - "exact recovery in the stochastic block model"
    - "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-6 (induced by source paper "optimal laplacian regularization for sparse spectral community detection"):
  with members:
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "spectral redemption in clustering sparse networks"
    - "community detection thresholds and the weak ramanujan property"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-7 (induced by source paper "spectral embedding of regularized block models"):
  with members:
    - "weighted spectral embedding of graphs"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "clustering in partially labeled stochastic block models via total variation minimization"):
  with members:
    - "semi supervised learning via sparse label propagation"

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)