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 in the labelled stochastic block model"
  Abstract: We consider the problem of community detection from observed interactions between individuals, in the context where multiple types of interaction are possible. We use labelled stochastic block models to represent the observed data, where labels correspond to interaction types. Focusing on a two-community scenario, we conjecture a threshold for the problem of reconstructing the hidden communities in a way that is correlated with the true partition. To substantiate the conjecture, we prove that the given threshold correctly identifies a transition on the behaviour of belief propagation from insensitive to sensitive. We further prove that the same threshold corresponds to the transition in a related inference problem on a tree model from infeasible to feasible. Finally, numerical results using belief propagation for community detection give further support to the conjecture.

Connected through hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"

Connected through hyperedge HE-2 (induced by source paper "generalized matrix means for semi supervised learning with multilayer graphs"):
  with members:
    - "spectral clustering of signed graphs via matrix power means"
    - "fast randomized semi supervised clustering"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-3 (induced by source paper "information theoretic limits for community detection in network models"):
  with members:
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "exact recovery in the stochastic block model"
    - "efficient and principled method for detecting communities in networks"

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"
    - "spectral detection on sparse hypergraphs"

Connected through hyperedge HE-5 (induced by source paper "regular decomposition an information and graph theoretic approach to stochastic block models"):
  with members:
    - "analysis of large sparse graphs using regular decomposition of graph distance matrices"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "clustering from sparse pairwise measurements"):
  with members:
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "spectral detection in the censored block model"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "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 "asymptotic mutual information for the two groups stochastic block model"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "spectral detection in the censored block model"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"
    - "near optimal joint object matching via convex relaxation"
    - "accurate community detection in the stochastic block model via spectral algorithms"
    - "tight error bounds for structured prediction"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"

Connected through hyperedge HE-8 (induced by source paper "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"):
  with members:
    - "asymptotic mutual information for the two groups stochastic block model"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "renyi divergence and kullback leibler divergence"
    - "exact recovery in the stochastic block model"
    - "pseudo likelihood methods for community detection in large sparse networks"
    - "community detection thresholds and the weak ramanujan property"

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)