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: "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
  Abstract: The rate-distortion saddle-point problem considered by Lapidoth (1997) consists in finding the minimum rate to compress an arbitrary ergodic source when one is constrained to use a random Gaussian codebook and minimum (Euclidean) distance encoding is employed. We extend Lapidoth's analysis in several directions in this paper. Firstly, we consider refined asymptotics. In particular, when the source is stationary and memoryless, we establish the second-order, moderate, and large deviation asymptotics of the problem. Secondly, by "random Gaussian codebook", Lapidoth refers to a collection of random codewords, each of which is drawn independently and uniformly from the surface of an $n$-dimensional sphere. To be more precise, we term this as a spherical Gaussian codebook. We also consider i.i.d.\ Gaussian codebooks in which each random codeword is drawn independently from a product Gaussian distribution. We derive the second-order, moderate, and large deviation asymptotics when i.i.d.\ Gaussian codebooks are employed. Interestingly, in contrast to the recent work on the channel coding counterpart by Scarlett, Tan and Durisi (2017), the dispersions for spherical and i.i.d.\ Gaussian codebooks are identical. Our bounds on the optimal error exponent for the spherical case coincide on a non-empty interval of rates above the rate-distortion function. The optimal error exponent for the i.i.d.\ case is established for all rates.

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"
    - "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"
    - "the rate distortion risk in estimation from compressed data"

Connected through hyperedge HE-2 (induced by source paper "exponential strong converse for content identification with lossy recovery"):
  with members:
    - "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"
    - "smoothing brascamp lieb inequalities and strong converses for common randomness generation"

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)