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: "distributed channel synthesis"
  Abstract: Two familiar notions of correlation are rediscovered as the extreme operating points for distributed synthesis of a discrete memoryless channel, in which a stochastic channel output is generated based on a compressed description of the channel input. Wyner's common information is the minimum description rate needed. However, when common randomness independent of the input is available, the necessary description rate reduces to Shannon's mutual information. This paper characterizes the optimal tradeoff between the amount of common randomness used and the required rate of description. We also include a number of related derivations, including the effect of limited local randomness, rate requirements for secrecy, applications to game theory, and new insights into common information duality. Our proof makes use of a soft covering lemma, known in the literature for its role in quantifying the resolvability of a channel. The direct proof (achievability) constructs a feasible joint distribution over all parts of the system using a soft covering, from which the behavior of the encoder and decoder is inferred, with no explicit reference to joint typicality or binning. Of auxiliary interest, this paper also generalizes and strengthens this soft covering tool.

Connected through hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "mean estimation from one bit measurements"
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"

Connected through hyperedge HE-2 (induced by source paper "keyless covert communication via channel state information"):
  with members:
    - "first and second order asymptotics in covert communication"
    - "stealthy secret key generation"
    - "wiretap channels with causal state information strong secrecy"
    - "covert communication with channel state information at the transmitter"
    - "fundamental limits of communication with low probability of detection"
    - "the likelihood encoder for lossy compression"
    - "wiretap channels with random states non causally available at the encoder"
    - "covert communication over noisy channels a resolvability perspective"

Connected through hyperedge HE-3 (induced by source paper "undetectable radios covert communication under spectral mask constraints"):
  with members:
    - "covert communication over a compound channel"
    - "first and second order asymptotics in covert communication"
    - "embedding covert information in broadcast communications"
    - "covert communication over adversarially jammed channels"
    - "covert communication over a k user multiple access channel"
    - "time division is optimal for covert communication over some broadcast channels"
    - "covert communication with channel state information at the transmitter"
    - "fundamental limits of communication with low probability of detection"

Connected through hyperedge HE-4 (induced by source paper "communication for generating correlation a unifying survey"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "exact exponent for soft covering"
    - "exact channel synthesis"
    - "strong converse using change of measure arguments"
    - "coding for positive rate in the source model key agreement problem"
    - "wiretap channels nonasymptotic fundamental limits"
    - "channel resolvability theorems for general sources and channels"
    - "strong functional representation lemma and applications to coding theorems"

Connected through hyperedge HE-5 (induced by source paper "communication for generating correlation a survey"):
  with members:
    - "secret key capacity for multipleaccess channel with public feedback"
    - "exact soft covering exponent"
    - "on exact and infty r enyi common informations"
    - "exact channel synthesis"
    - "strong converse using change of measure arguments"
    - "coding for positive rate in the source model key agreement problem"
    - "wiretap channels nonasymptotic fundamental limits"
    - "channel resolvability theorems for general sources and channels"

Connected through hyperedge HE-6 (induced by source paper "privacy aware distributed hypothesis testing"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "distributed hypothesis testing with privacy constraints"
    - "technical privacy metrics a systematic survey"
    - "distributed hypothesis testing based on unequal error protection codes"
    - "privacy against a hypothesis testing adversary"
    - "on the reliability function of distributed hypothesis testing under optimal detection"
    - "distributed hypothesis testing over discrete memoryless channels"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"

Connected through hyperedge HE-7 (induced by source paper "coordination through shared randomness"):
  with members:
    - "communication for generating correlation a survey"
    - "on the optimality of secret key agreement via omniscience"
    - "polar coding for empirical coordination of signals and actions over noisy channels"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "secure cascade channel synthesis"
    - "on non interactive simulation of joint distributions"
    - "empirical coordination with two sided state information and correlated source and state"
    - "empirical coordination with channel feedback and strictly causal or causal encoding"

Connected through hyperedge HE-8 (induced by source paper "distributed source simulation with no communication"):
  with members:
    - "decidability of non interactive simulation of joint distributions"
    - "secure cascade channel synthesis"
    - "on non interactive simulation of joint distributions"
    - "a stronger soft covering lemma and applications"
    - "zero error coordination"
    - "exact common information"
    - "analysis of boolean functions"
    - "simulation of a channel with another channel"

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)