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: "spectral detection on sparse hypergraphs"
  Abstract: We consider the problem of the assignment of nodes into communities from a set of hyperedges, where every hyperedge is a noisy observation of the community assignment of the adjacent nodes. We focus in particular on the sparse regime where the number of edges is of the same order as the number of vertices. We propose a spectral method based on a generalization of the non-backtracking Hashimoto matrix into hypergraphs. We analyze its performance on a planted generative model and compare it with other spectral methods and with Bayesian belief propagation (which was conjectured to be asymptotically optimal for this model). We conclude that the proposed spectral method detects communities whenever belief propagation does, while having the important advantages to be simpler, entirely nonparametric, and to be able to learn the rule according to which the hyperedges were generated without prior information.

Connected through hyperedge HE-1 (induced by source paper "landing probabilities of random walks for seed set expansion in hypergraphs"):
  with members:
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "optimizing generalized pagerank methods for seed expansion community detection"
    - "quadratic decomposable submodular function minimization"
    - "inhomogoenous hypergraph clustering with applications"
    - "the spacey random walk a stochastic process for higher order data"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "exact recovery in the hypergraph stochastic block model a spectral algorithm"):
  with members:
    - "on the minimax misclassification ratio of hypergraph community detection"
    - "recovering nonuniform planted partitions via iterated projection"
    - "community recovery in hypergraphs"
    - "a simple spectral algorithm for recovering planted partitions"
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-3 (induced by source paper "on the minimax misclassification ratio of hypergraph community detection"):
  with members:
    - "community recovery in hypergraphs"
    - "motif clustering and overlapping clustering for social network analysis"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-4 (induced by source paper "community recovery in hypergraphs"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "community recovery in graphs with locality"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "clustering with same cluster queries"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-5 (induced by source paper "optimal inference in crowdsourced classification via belief propagation"):
  with members:
    - "optimality of belief propagation for crowdsourced classification"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "max product belief propagation for linear programming applications to combinatorial optimization"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-6 (induced by source paper "motif clustering and overlapping clustering for social network analysis"):
  with members:
    - "higher order organization of complex networks"
    - "parallel correlation clustering on big graphs"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "a tutorial on spectral clustering"

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)