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: "exponent function for source coding with side information at the decoder at rates below the rate distortion function"
  Abstract: We consider the rate distortion problem with side information at the decoder posed and investigated by Wyner and Ziv. The rate distortion function indicating the trade-off between the rate on the data compression and the quality of data obtained at the decoder was determined by Wyner and Ziv. In this paper, we study the error probability of decoding at rates below the rate distortion function. We evaluate the probability of decoding such that the estimation of source outputs by the decoder has a distortion not exceeding a prescribed distortion level. We prove that when the rate of the data compression is below the rate distortion function this probability goes to zero exponentially and derive an explicit lower bound of this exponent function. On the Wyner-Ziv source coding problem the strong converse coding theorem has not been established yet. We prove this as a simple corollary of our result.

Connected through hyperedge HE-1 (induced by source paper "a new proof of nonsignalling multiprover parallel repetition theorem"):
  with members:
    - "strong converse using change of measure arguments"

Connected through hyperedge HE-2 (induced by source paper "strong converse using change of measure arguments"):
  with members:
    - "a new proof of nonsignalling multiprover parallel repetition theorem"
    - "communication for generating correlation a survey"
    - "communication for generating correlation a unifying survey"
    - "improved finite blocklength converses for slepian wolf coding via linear programming"
    - "second order region for gray wyner network"
    - "strong converses are just edge removal properties"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "wiretap channel capacity secrecy criteria strong converse and phase change"

Connected through hyperedge HE-3 (induced by source paper "strong converse for hypothesis testing against independence over a two hop network"):
  with members:
    - "exponent function for one helper source coding problem at rates outside the rate region"

Connected through hyperedge HE-4 (induced by source paper "exponential strong converse for content identification with lossy recovery"):
  with members:
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "second order region for gray wyner network"
    - "the dispersion of universal joint source channel coding for arbitrary sources and additive channels"
    - "non asymptotic converse bounds and refined asymptotics for two lossy source coding problems"
    - "wyner s common information under r enyi divergence measures"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "new strong converse for asymmetric broadcast channels"
    - "achievable moderate deviations asymptotics for streaming compression of correlated sources"

Connected through hyperedge HE-5 (induced by source paper "wyner s common information under r enyi divergence measures"):
  with members:
    - "second order region for gray wyner network"
    - "exact random coding secrecy exponents for the wiretap channel"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "analysis of remaining uncertainties and exponents under various conditional r e nyi entropies"
    - "on the renyi divergence joint range of relative entropies and a channel coding theorem"
    - "exact common information"
    - "renyi divergence and kullback leibler divergence"
    - "effective secrecy reliability confusion and stealth"

Connected through hyperedge HE-6 (induced by source paper "partial strong converse for the non degraded wiretap channel"):
  with members:
    - "new strong converse for asymmetric broadcast channels"
    - "strong converse exponent for state dependent channels with full state information at the sender and partial state information at the receiver"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "strong converse exponent for degraded broadcast channels at rates outside the capacity region"
    - "equal image size source partitioning creating strong fano s inequalities for multi terminal discrete memoryless channels"
    - "strong converse theorems for degraded broadcast channels with feedback"
    - "converses for secret key agreement and secure computing"
    - "information spectrum approach to strong converse theorems for degraded wiretap channels"

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)