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: "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies"
  Abstract: In this paper, we analyze the asymptotics of the normalized remaining uncertainty of a source when a compressed or hashed version of it and correlated side-information is observed. For this system, commonly known as Slepian-Wolf source coding, we establish the optimal (minimum) rate of compression of the source to ensure that the remaining uncertainties vanish. We also study the exponential rate of decay of the remaining uncertainty to zero when the rate is above the optimal rate of compression. In our study, we consider various classes of random universal hash functions. Instead of measuring remaining uncertainties using traditional Shannon information measures, we do so using two forms of the conditional R\'{e}nyi entropy. Among other techniques, we employ new one-shot bounds and the moments of type class enumerator method for these evaluations. We show that these asymptotic results are generalizations of the strong converse exponent and the error exponent of the Slepian-Wolf problem under maximum \emph{a posteriori} (MAP) decoding.

Connected through hyperedge HE-1 (induced by source paper "the renyi capacity and center"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "the sphere packing bound for memoryless channels"
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "the sphere packing bound for dspcs with feedback a la augustin"
    - "the augustin capacity and center"
    - "the augustin center and the sphere packing bound for memoryless channels"
    - "on renyi entropy power inequalities"
    - "strong converse theorems for classes of multimessage multicast networks a renyi divergence approach"

Connected through hyperedge HE-2 (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"
    - "asymptotic coupling and its applications in information theory"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "wyner s common information under r enyi divergence measures"):
  with members:
    - "second order region for gray wyner network"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "exponent function for source coding with side information at the decoder at rates below the rate distortion function"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "exact common information"
    - "renyi divergence and kullback leibler divergence"
    - "effective secrecy reliability confusion and stealth"

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)