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 "renyi entropy bounds on the active learning cost performance tradeoff"):
  Abstract: Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the R\'enyi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.

Includes members:
  Member "graph powering and spectral robustness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")
  Member "a survey on theoretical advances of community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graph generators state of the art and open challenges")
  Member "connectivity in random annulus graphs and the geometric block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active learning in the geometric block model")
  Member "active community detection a maximum likelihood approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "characterization of citizens using word2vec and latent topic analysis in a large set of tweets")
  Member "active learning for community detection in stochastic block models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "s2 an efficient graph based active learning algorithm with application to nonparametric classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "active learning in the overparameterized and interpolating regime")
  Member "phase transitions in semisupervised clustering of sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual information in community detection with covariate information and correlated networks")
  Member "exact recovery in the stochastic block model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "local access to huge random objects through partial sampling")

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)