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: "source and channel simulation using arbitrary randomness"
  Abstract: Necessary and sufficient conditions for approximation of a general channel by a general source are proved. For the special case in which the channel input is deterministic, which corresponds to source simulation, we prove a stronger necessary condition. As the approximation criteria, vanishing variational distance between the original and the approximated quantity is used for both of the problems. Both necessary and sufficient conditions for the two problems are based on some individual properties of the sources and the channel and are relatively easy to evaluate. In particular, unlike prior results for this problem, our results do not require solving an optimization problem to test simulatability. The results are illustrated with several non-ergodic examples.

Connected through hyperedge HE-1 (induced by source paper "interval algorithm for random number generation information spectrum approach"):
  with members:
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"
    - "information spectrum approach to second order coding rate in channel coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-2 (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"
    - "asymptotic coupling and its applications in information theory"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "simulation of a channel with another channel"):
  with members:
    - "zero error coordination"
    - "exact common information"
    - "renyi divergence and kullback leibler divergence"
    - "distributed channel synthesis"
    - "achievability proof via output statistics of random binning"
    - "channel simulation via interactive communications"
    - "hybrid codes needed for coordination over the point to point channel"
    - "a new data processing inequality and its applications in distributed source and channel coding"

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)