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: "universal channel coding for exponential family of channels"
  Abstract: We propose a universal channel coding when each output distribution forms an exponential family even in a continuous output system. We propose two types of universal codes; One has the exponentially decreasing error with explicit a lower bound for the error exponent. The other attains the $\epsilon$-capacity up to the second order. Our encoder is the same as the previous paper [CMP {\bf 289}, 1087]. For our decoding process, we invent $\alpha$-R\'{e}nyi divergence version of Clarke and Barron's formula for Bayesian average distribution, which are not required in the previous paper. Combing this formula and the information spectrum method, we propose our universal decoder. Our method enables us to treat a universal code of the continuous and discrete cases in a unified way with the second order coding rate as well as with the exponential decreasing rate.

Connected through hyperedge HE-1 (induced by source paper "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "two measures of dependence"
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "equivocations exponents and second order coding rates under various r enyi information measures"
    - "universal channel coding for general output alphabet"
    - "converses for secret key agreement and secure computing"
    - "encoding tasks and renyi entropy"
    - "renyi divergence and kullback leibler divergence"

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)