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: "asymptotic coupling and its applications in information theory"
  Abstract: A coupling of two distributions $P_{X}$ and $P_{Y}$ is a joint distribution $P_{XY}$ with marginal distributions equal to $P_{X}$ and $P_{Y}$. Given marginals $P_{X}$ and $P_{Y}$ and a real-valued function $f(P_{XY})$ of the joint distribution $P_{XY}$, what is its minimum over all couplings $P_{XY}$ of $P_{X}$ and $P_{Y}$? We study the asymptotics of such coupling problems with different $f$'s. These include the maximal coupling, minimum distance coupling, maximal guessing coupling, and minimum entropy coupling problems. We characterize the limiting values of these coupling problems as the number of copies of $X$ and $Y$ tends to infinity. We show that they typically converge at least exponentially fast to their limits. Moreover, for the problems of maximal coupling and minimum excess-distance probability coupling, we also characterize (or bound) the optimal convergence rates (exponents). Furthermore, for the maximal guessing coupling problem we show that it is equivalent to the probability distribution approximation problem. Therefore, some existing results the latter problem can be used to derive the asymptotics of the maximal guessing coupling problem. We also study the asymptotics of the maximal guessing coupling problem for two \emph{general} sources and a generalization of this problem, named the \emph{maximal guessing coupling through a channel problem}. We apply the preceding results to several new information-theoretic problems, including exact intrinsic randomness, exact resolvability, channel capacity with input distribution constraint, and perfect stealth and secrecy communication.

Connected through hyperedge HE-1 (induced by source paper "simulation of random variables under r enyi divergence measures of all orders"):
  with members:
    - "on exact and infty r enyi common informations"
    - "renyi differential privacy"
    - "differential privacy as a mutual information constraint"
    - "renyi divergence and kullback leibler divergence"
    - "reliability and secrecy functions of the wiretap channel under cost constraint"
    - "privacy against statistical inference"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "general classes of lower bounds on the probability of error in multiple hypothesis testing"

Connected through hyperedge HE-2 (induced by source paper "generalized fano type inequality for countably infinite systems with list decoding"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "concentration of measure inequalities in information theory communications and coding second edition"
    - "renyi divergence and kullback leibler divergence"
    - "some properties of renyi entropy over countably infinite alphabets"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"

Connected through hyperedge HE-3 (induced by source paper "r enyi resolvability and its applications to the wiretap channel"):
  with members:
    - "corrections to wyner s common information under r enyi divergence measures"
    - "exact channel synthesis"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies"
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "distributed simulation of continuous random variables"
    - "coding schemes for achieving strong secrecy at negligible cost"

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)