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: "single letter characterization of epsilon capacity for mixed memoryless channels"
  Abstract: For the class of mixed channels decomposed into stationary memoryless channels, single-letter characterizations of the $\varepsilon$-capacity have not been known except for restricted classes of channels such as the regular decomposable channel introduced by Winkelbauer. This paper gives single-letter characterizations of $\varepsilon$-capacity for mixed channels decomposed into at most countably many memoryless channels with a finite input alphabet and a general output alphabet with/without cost constraints. It is shown that a given characterization reduces to the one for the channel capacity given by Ahlswede when $\varepsilon$ is zero. In the proof of the coding theorem, the meta converse bound, originally given by Polyanskiy, Poor and Verd\'u, is particularized for the mixed channel decomposed into general component channels.

Connected through hyperedge HE-1 (induced by source paper "first and second order coding theorems for mixed memoryless channels with general mixture"):
  with members:
    - "second order coding rates for channels with state"
    - "quasi static simo fading channels at finite blocklength"
    - "a tight upper bound for the third order asymptotics for most discrete memoryless channels"
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"
    - "second order slepian wolf coding theorems for non mixed and mixed sources"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-2 (induced by source paper "universal channel coding for exponential family of channels"):
  with members:
    - "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions"
    - "second order coding rates for channels with state"
    - "information spectrum approach to second order coding rate in channel coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

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)