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 and matrix completion with two sided graph side information"):
  Abstract: We consider the problem of recovering communities of users and communities of items (such as movies) based on a partially observed rating matrix as well as side-information in the form of similarity graphs of the users and items. The user-to-user and item-to-item similarity graphs are generated according to the celebrated stochastic block model (SBM). We develop lower and upper bounds on the minimum expected number of observed ratings (also known as the sample complexity) needed for this recovery task. These bounds are functions of various parameters including the quality of the graph side-information which is manifested in the intra- and inter-cluster probabilities of the SBMs. We show that these bounds match for a wide range of parameters of interest, and match up to a constant factor of two for the remaining parameter regime. Our information-theoretic results quantify the benefits of the two-sided graph side-information for recovery, and further analysis reveals that the two pieces of graph side-information produce an interesting synergistic effect under certain scenarios. This means that if one observes only one of the two graphs, then the required sample complexity worsens to the case in which none of the graphs is observed. Thus both graphs are strictly needed to reduce the sample complexity.

Includes members:
  Member "recovering a single community with side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exit analysis for community detection")
  Member "community detection with side information exact recovery under the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mutual information in community detection with covariate information and correlated networks")
  Member "information theoretic bounds for exact recovery in weighted stochastic block models using the renyi divergence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral clustering of signed graphs via matrix power means")
  Member "matrix completion on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "context aware visual compatibility prediction")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "renyi entropy bounds on the active learning cost performance tradeoff")
  Member "community detection in networks with node attributes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding temporal patterns using algebraic fingerprints")

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)