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: "r enyi divergence and majorization"
  Abstract: R\'enyi divergence is related to R\'enyi entropy much like information divergence (also called Kullback-Leibler divergence or relative entropy) is related to Shannon's entropy, and comes up in many settings. It was introduced by R\'enyi as a measure of information that satisfies almost the same axioms as information divergence. We review the most important properties of R\'enyi divergence, including its relation to some other distances. We show how R\'enyi divergence appears when the theory of majorization is generalized from the finite to the continuous setting. Finally, R\'enyi divergence plays a role in analyzing the number of binary questions required to guess the values of a sequence of random variables.

Connected through hyperedge HE-1 (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"
    - "information theoretic applications of the logarithmic probability comparison bound"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "renyi divergence and kullback leibler divergence"):
  with members:
    - "multiple source adaptation and the renyi divergence"
    - "on pinsker s and vajda s type inequalities for csiszar s f divergences"
    - "a note on a characterization of renyi measures and its relation to composite hypothesis testing"

Connected through hyperedge HE-3 (induced by source paper "information theoretic applications of the logarithmic probability comparison bound"):
  with members:
    - "on zero rate error exponents of finite state channels with input dependent states"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-4 (induced by source paper "some properties of renyi entropy over countably infinite alphabets"):
  with members:
    - "universal coding on infinite alphabets exponentially decreasing envelopes"

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)