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 "side information in the binary stochastic block model exact recovery"):
  Abstract: In the community detection problem, one may have access to additional observations (side information) about the label of each node. This paper studies the effect of the quality and quantity of side information on the phase transition of exact recovery in the binary symmetric stochastic block model (SBM) with $n$ nodes. When the side information consists of the label observed through a binary symmetric channel with crossover probability $\alpha$, and when $\log(\frac{1-\alpha}{\alpha}) =O(\log(n))$, it is shown that side information has a positive effect on phase transition; the new phase transition under this condition is characterized. When $\alpha$ is constant or approaches zero sufficiently slowly, i.e., $\log(\frac{1-\alpha}{\alpha}) = o(\log(n))$, it is shown that side information does not help exact recovery. When the side information consists of the label observed through a binary erasure channel with parameter $\epsilon$, and when $\log(\epsilon)=O(\log(n))$, it is shown that side information improves exact recovery and the new phase transition is characterized. If $\log(\epsilon)=o(\log(n))$, then it is shown that side information is not helpful. The results are then generalized to an arbitrary side information of finite cardinality. Necessary and sufficient conditions are derived for exact recovery that are tight, except for one special case under $M$-ary side information. An efficient algorithm that incorporates the effect of side information is proposed that uses a partial recovery algorithm combined with a local improvement procedure. Sufficient conditions are derived for exact recovery under this efficient algorithm.

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)