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 "achievability proof via output statistics of random binning"):
  Abstract: This paper introduces a new and ubiquitous framework for establishing achievability results in \emph{network information theory} (NIT) problems. The framework uses random binning arguments and is based on a duality between channel and source coding problems. {Further,} the framework uses pmf approximation arguments instead of counting and typicality. This allows for proving coordination and \emph{strong} secrecy problems where certain statistical conditions on the distribution of random variables need to be satisfied. These statistical conditions include independence between messages and eavesdropper's observations in secrecy problems and closeness to a certain distribution (usually, i.i.d. distribution) in coordination problems. One important feature of the framework is to enable one {to} add an eavesdropper and obtain a result on the secrecy rates "for free." #R##N#We make a case for generality of the framework by studying examples in the variety of settings containing channel coding, lossy source coding, joint source-channel coding, coordination, strong secrecy, feedback and relaying. In particular, by investigating the framework for the lossy source coding problem over broadcast channel, it is shown that the new framework provides a simple alternative scheme to \emph{hybrid} coding scheme. Also, new results on secrecy rate region (under strong secrecy criterion) of wiretap broadcast channel and wiretap relay channel are derived. In a set of accompanied papers, we have shown the usefulness of the framework to establish achievability results for coordination problems including interactive channel simulation, coordination via relay and channel simulation via another channel.

Includes members:
  Member "when is it possible to simulate a dmc channel from another":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strong coordination over noisy channels is separation sufficient")
  Member "distributed channel synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "hybrid coding an interface for joint source channel coding and network communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the likelihood encoder for lossy compression")
  Member "channel simulation via interactive communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication for generating correlation a unifying survey")
  Member "coordination via a relay":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed source simulation with no communication")
  Member "strongly secure communications over the two way wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secrecy rates for a channel with two senders and two receivers")
  Member "strong secrecy from channel resolvability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "securing distributed function approximation via coding for continuous compound channels")
  Member "construction of slepian wolf source code and broadcast channel code based on hash property":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi terminal codes using constrained random number generators")

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)