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: "concentration of measure inequalities and their communication and information theoretic applications"
  Abstract: During the last two decades, concentration of measure has been a subject of various exciting developments in convex geometry, functional analysis, statistical physics, high-dimensional statistics, probability theory, information theory, communications and coding theory, computer science, and learning theory. One common theme which emerges in these fields is probabilistic stability: complicated, nonlinear functions of a large number of independent or weakly dependent random variables often tend to concentrate sharply around their expected values. Information theory plays a key role in the derivation of concentration inequalities. Indeed, both the entropy method and the approach based on transportation-cost inequalities are two major information-theoretic paths toward proving concentration. #R##N#This brief survey is based on a recent monograph of the authors in the Foundations and Trends in Communications and Information Theory (online available at this http URL), and a tutorial given by the authors at ISIT 2015. It introduces information theorists to three main techniques for deriving concentration inequalities: the martingale method, the entropy method, and the transportation-cost inequalities. Some applications in information theory, communications, and coding theory are used to illustrate the main ideas.

Connected through hyperedge HE-1 (induced by source paper "f divergence inequalities"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "optimal quantization for distribution synthesis"
    - "e_ gamma resolvability"
    - "bounds between contraction coefficients"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "refinement of some moment inequalities"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"

Connected through hyperedge HE-2 (induced by source paper "bounds among f divergences"):
  with members:
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "f divergence inequalities"
    - "optimal quantization for distribution synthesis"
    - "on the r e nyi divergence and the joint range of relative entropies"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "resolvability in e γ with applications to lossy compression and wiretap channels"
    - "tight bounds for symmetric divergence measures and a refined bound for lossless source coding"

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)