human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "information theoretic applications of the logarithmic probability comparison bound"
  Abstract: A well-known technique in estimating probabilities of rare events in general and in information theory in particular (used, e.g., in the sphere-packing bound), is that of finding a reference probability measure under which the event of interest has probability of order one and estimating the probability in question by means of the Kullback-Leibler divergence. A method has recently been proposed in [2], that can be viewed as an extension of this idea in which the probability under the reference measure may itself be decaying exponentially, and the Renyi divergence is used instead. The purpose of this paper is to demonstrate the usefulness of this approach in various information-theoretic settings. For the problem of channel coding, we provide a general methodology for obtaining matched, mismatched and robust error exponent bounds, as well as new results in a variety of particular channel models. Other applications we address include rate-distortion coding and the problem of guessing.

Connected through hyperedge HE-1 (induced by source paper "information theoretic perspectives on brascamp lieb inequality and its reverse"):
  with members:
    - "arimoto r enyi conditional entropy and bayesian m ary hypothesis testing"
    - "strengthening the entropy power inequality"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "brascamp lieb inequality and its reverse an information theoretic view"
    - "converses for distributed estimation via strong data processing inequalities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "on non interactive simulation of joint distributions"
    - "a note on the strong data processing inequalities in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "brascamp lieb inequality and its reverse an information theoretic view"):
  with members:
    - "information theoretic perspectives on brascamp lieb inequality and its reverse"
    - "converses for distributed estimation via strong data processing inequalities"
    - "secret key generation with one communicator and a one shot converse via hypercontractivity"
    - "a note on the strong data processing inequalities in bayesian networks"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "an extremal inequality for long markov chains"
    - "key capacity for product sources with application to stationary gaussian processes"
    - "wyner s common information generalizations and a new lossy source coding interpretation"

Connected through hyperedge HE-3 (induced by source paper "on the renyi divergence joint range of relative entropies and a channel coding theorem"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "bounds among f divergences"
    - "upper bounds on the relative entropy and r enyi divergence as a function of total variation distance for finite alphabets"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "renyi divergence and kullback leibler divergence"
    - "multiple source adaptation and the renyi divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"
    - "r enyi divergence and majorization"

Connected through hyperedge HE-4 (induced by source paper "on the r e nyi divergence and the joint range of relative entropies"):
  with members:
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"
    - "minimization problems based on a parametric family of relative entropies ii reverse projection"
    - "on pairs of f divergences and their joint range"
    - "a note on a characterization of renyi measures and its relation to composite hypothesis testing"
    - "guessing under source uncertainty"
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"

Question: Please tell me which class the center node 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)