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: "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
  Abstract: We derive sharp thresholds for exact recovery of communities in a weighted stochastic block model, where observations are collected in the form of a weighted adjacency matrix, and the weight of each edge is generated independently from a distribution determined by the community membership of its endpoints. Our main result, characterizing the precise boundary between success and failure of maximum likelihood estimation when edge weights are drawn from discrete distributions, involves the Renyi divergence of order $\frac{1}{2}$ between the distributions of within-community and between-community edges. When the Renyi divergence is above a certain threshold, meaning the edge distributions are sufficiently separated, maximum likelihood succeeds with probability tending to 1; when the Renyi divergence is below the threshold, maximum likelihood fails with probability bounded away from 0. In the language of graphical channels, the Renyi divergence pinpoints the information-theoretic capacity of discrete graphical channels with binary inputs. Our results generalize previously established thresholds derived specifically for unweighted block models, and support an important natural intuition relating the intrinsic hardness of community estimation to the problem of edge classification. Along the way, we establish a general relationship between the Renyi divergence and the probability of success of the maximum likelihood estimator for arbitrary edge weight distributions. Finally, we discuss consequences of our bounds for the related problems of censored block models and submatrix localization, which may be seen as special cases of the framework developed in our paper.

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 "community detection and matrix completion with two sided graph side information"):
  with members:
    - "recovering a single community with side information"
    - "community detection with side information exact recovery under the stochastic block model"
    - "matrix completion on graphs"
    - "exact recovery in the stochastic block model"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-3 (induced by source paper "exact recovery for a family of community detection generative models"):
  with members:
    - "does robustness imply tractability a lower bound for planted clique in the semi random model"
    - "community detection in hypergraphs spiked tensor models and sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-4 (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"
    - "spectral detection on sparse hypergraphs"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-5 (induced by source paper "information theoretic limits for community detection in network models"):
  with members:
    - "exact recovery in the stochastic block model"
    - "community detection in the labelled stochastic block model"
    - "efficient and principled method for detecting communities in networks"

Connected through hyperedge HE-6 (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"
    - "spectral detection on sparse hypergraphs"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-7 (induced by source paper "community detection with colored edges"):
  with members:
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-8 (induced by source paper "community recovery in graphs with locality"):
  with members:
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "information recovery from pairwise measurements a shannon theoretic approach"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "optimal haplotype assembly from high throughput mate pair reads"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "near optimal joint object matching via convex relaxation"
    - "decoding binary node labels from censored edge measurements phase transition and efficient recovery"
    - "exact recovery in the stochastic block model"

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.IT(Information Theory)