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 with side information exact recovery under the stochastic block model"
  Abstract: The community detection problem involves making inferences about node labels in a graph, based on observing the graph edges. This paper studies the effect of additional, nongraphical side information on the phase transition of exact recovery in the binary stochastic block model with   $n$    nodes. When side information consists of noisy labels with error probability    $\alpha$  , it is shown that phase transition is improved if and only if   $\log (\frac{1-\alpha }{\alpha })=\Omega (\log (n))$   . When side information consists of revealing a fraction    $1-\epsilon$   of the labels, it is shown that phase transition is improved if and only if   $\log (1/\epsilon)=\Omega (\log (n))$  . For a more general side information consisting of   $K$   features, two scenarios are studied, first,   $K$    is fixed while the likelihood of each feature with respect to corresponding node label evolves with   $n$  , and, second, the number of features   $K$   varies with    $n$   but the likelihood of each feature is fixed. In each case, we find when side information improves the exact recovery phase transition and by how much. In the process of deriving inner bounds, a variation of an efficient algorithm is proposed for community detection with side information that uses a partial recovery algorithm combined with a local improvement procedure.

Connected through hyperedge HE-1 (induced by source paper "mutual information in community detection with covariate information and correlated networks"):
  with members:
    - "the geometry of community detection via the mmse matrix"
    - "information theoretic thresholds from the cavity method"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "structure and inference in annotated networks"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "phase transitions in community detection a solvable toy model"

Connected through hyperedge HE-2 (induced by source paper "exit analysis for community detection"):
  with members:
    - "recovering a single community with side information"
    - "thinking fast and slow with deep learning and tree search"
    - "the power of side information in subgraph detection"
    - "exact recovery in the stochastic block model"
    - "a tensor approach to learning mixed membership community models"
    - "comparative study for inference of hidden classes in stochastic block models"

Connected through hyperedge HE-3 (induced by source paper "community detection and matrix completion with two sided graph side information"):
  with members:
    - "recovering a single community with side information"
    - "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence"
    - "matrix completion on graphs"
    - "exact recovery in the stochastic block model"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-4 (induced by source paper "privacy of dependent users against statistical matching"):
  with members:
    - "asymptotic limits of privacy in bayesian time series matching"
    - "a concentration of measure approach to database de anonymization"
    - "single server single message online private information retrieval with side information"
    - "asymptotic loss in privacy due to dependency in gaussian traces"
    - "fundamental limits of database alignment"
    - "matching graphs with community structure a concentration of measure approach"
    - "on the performance of a canonical labeling for matching correlated erdős renyi graphs"
    - "typicality matching for pairs of correlated graphs"

Connected through hyperedge HE-5 (induced by source paper "recovering a single community with side information"):
  with members:
    - "the power of side information in subgraph detection"
    - "a tensor approach to learning mixed membership community models"
    - "comparative study for inference of hidden classes in stochastic block models"

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)