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 "on the renyi divergence joint range of relative entropies and a channel coding theorem"):
  Abstract: This paper starts by considering the minimization of the Renyi divergence subject to a constraint on the total variation distance. Based on the solution of this optimization problem, the exact locus of the points $\bigl( D(Q\|P_1), D(Q\|P_2) \bigr)$ is determined when $P_1, P_2, Q$ are arbitrary probability measures which are mutually absolutely continuous, and the total variation distance between $P_1$ and $P_2$ is not below a given value. It is further shown that all the points of this convex region are attained by probability measures which are defined on a binary alphabet. This characterization yields a geometric interpretation of the minimal Chernoff information subject to a constraint on the total variation distance. This paper also derives an exponential upper bound on the performance of binary linear block codes (or code ensembles) under maximum-likelihood decoding. Its derivation relies on the Gallager bounding technique, and it reproduces the Shulman-Feder bound as a special case. The bound is expressed in terms of the Renyi divergence from the normalized distance spectrum of the code (or the average distance spectrum of the ensemble) to the binomially distributed distance spectrum of the capacity-achieving ensemble of random block codes. This exponential bound provides a quantitative measure of the degradation in performance of binary linear block codes (or code ensembles) as a function of the deviation of their distance spectra from the binomial distribution. An efficient use of this bound is considered.

Includes members:
  Member "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the renyi capacity and center")
  Member "bounds among f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "empirical and strong coordination via soft covering with polar codes")
  Member "upper bounds on the relative entropy and r enyi divergence as a function of total variation distance for finite alphabets":
    also connected through other hyperedges:
      (none)
  Member "tight bounds for symmetric divergence measures and a refined bound for lossless source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "limits of deepfake detection a robust estimation viewpoint")
  Member "information theoretic applications of the logarithmic probability comparison bound":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse")
  Member "renyi divergence and kullback leibler divergence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "multiple source adaptation and the renyi divergence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learn to expect the unexpected probably approximately correct domain generalization")
  Member "on pinsker s and vajda s type inequalities for csiszar s f divergences":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theoretical evidence for adversarial robustness through randomization")

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)