human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "the power of side information in subgraph detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exit analysis for community detection")
  Member "structure and inference in annotated networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on a scalable entropic breaching of the overfitting barrier in machine learning")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "community detection via random and adaptive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dyncomm r package dynamic community detection for evolving networks")
  Member "a tensor approach to learning mixed membership community models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding a latent k simplex in o k nnz data time via subset smoothing")
  Member "community detection thresholds and the weak ramanujan property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "from tree matching to sparse graph alignment")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding temporal patterns using algebraic fingerprints")
  Member "comparative study for inference of hidden classes in stochastic block models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exit analysis for community detection")

Question: Please tell me which class the source paper 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)