human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the renyi capacity and center")
  Member "second order coding rates for channels with state":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "second order and moderate deviation analysis of a block fading channel with deterministic and energy harvesting power constraints")
  Member "single letter characterization of epsilon capacity for mixed memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "first and second order coding theorems for mixed memoryless channels with general mixture")
  Member "information spectrum approach to second order coding rate in channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite sample analysis of image registration")
  Member "second order asymptotics in fixed length source coding and intrinsic randomness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finite sample analysis of image registration")

Question: Please tell me which class the source paper 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)