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: "communication for generating correlation a survey"
  Abstract: The task of manipulating correlated random variables in a distributed setting has received attention in the fields of both Information Theory and Computer Science. Often shared correlations can be converted, using a little amount of communication, into perfectly shared uniform random variables. Such perfect shared randomness, in turn, enables the solutions of many tasks. Even the reverse conversion of perfectly shared uniform randomness into variables with a desired form of correlation turns out to be insightful and technically useful. In this survey article, we describe progress-to-date on such problems and lay out pertinent measures, achievability results, limits of performance, and point to new directions.

Connected through hyperedge HE-1 (induced by source paper "coordination through shared randomness"):
  with members:
    - "on the optimality of secret key agreement via omniscience"
    - "polar coding for empirical coordination of signals and actions over noisy channels"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "secure cascade channel synthesis"
    - "on non interactive simulation of joint distributions"
    - "empirical coordination with two sided state information and correlated source and state"
    - "empirical coordination with channel feedback and strictly causal or causal encoding"
    - "exact common information"

Connected through hyperedge HE-2 (induced by source paper "strong converse using change of measure arguments"):
  with members:
    - "a new proof of nonsignalling multiprover parallel repetition theorem"
    - "communication for generating correlation a unifying survey"
    - "improved finite blocklength converses for slepian wolf coding via linear programming"
    - "second order region for gray wyner network"
    - "strong converses are just edge removal properties"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "wiretap channel capacity secrecy criteria strong converse and phase change"
    - "linear programming based converses for finite blocklength lossy joint source channel 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)