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: "moderate deviations of lossy source coding for discrete and gaussian sources"
  Abstract: We study the moderate-deviations (MD) setting for lossy source coding of stationary memoryless sources. More specifically, we derive fundamental compression limits of source codes whose rates are $R(D) \pm \epsilon_n$, where $R(D)$ is the rate-distortion function and $\epsilon_n$ is a sequence that dominates $\sqrt{1/n}$. This MD setting is complementary to the large-deviations and central limit settings and was studied by Altug and Wagner for the channel coding setting. We show, for finite alphabet and Gaussian sources, that as in the central limit-type results, the so-called dispersion for lossy source coding plays a fundamental role in the MD setting for the lossy source coding problem.

Connected through hyperedge HE-1 (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-2 (induced by source paper "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"):
  with members:
    - "second order region for gray wyner network"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "second order coding region for the discrete lossy gray wyner source coding problem"
    - "the lossy common information of correlated sources"
    - "second order coding rates for channels with state"
    - "a technique for deriving one shot achievability results in network information theory"
    - "refinement of the random coding bound"

Connected through hyperedge HE-3 (induced by source paper "non asymptotic converse bounds and refined asymptotics for two lossy source coding problems"):
  with members:
    - "second order region for gray wyner network"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "second order coding rates for channels with state"
    - "second order coding rates for conditional rate distortion"
    - "a technique for deriving one shot achievability results in network information theory"
    - "moderate deviations in channel coding"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-4 (induced by source paper "moderate deviation asymptotics for variable length codes with feedback"):
  with members:
    - "streaming data transmission in the moderate deviations and central limit regimes"
    - "extrinsic jensen shannon divergence applications to variable length coding"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "moderate deviations in channel coding"
    - "opportunistic capacity and error exponent regions for compound channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "a simple converse of burnashev s reliability function"
    - "error exponents for variable length block codes with feedback and cost constraints"

Connected through hyperedge HE-5 (induced by source paper "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"):
  with members:
    - "the dispersion of nearest neighbor decoding for additive non gaussian channels"
    - "moderate deviations in channel coding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-6 (induced by source paper "second order and moderate deviation asymptotics for successive refinement"):
  with members:
    - "second order region for gray wyner network"
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "second order coding rates for channels with state"
    - "a case where interference does not affect the channel dispersion"
    - "on the dispersions of three network information theory problems"
    - "on the dispersions of the gel fand pinsker channel and dirty paper coding"

Connected through hyperedge HE-7 (induced by source paper "achievable moderate deviations asymptotics for streaming compression of correlated sources"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "exact moderate deviation asymptotics in streaming data transmission"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "streaming data transmission in the moderate deviations and central limit regimes"
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "the streaming dmt of fading channels"
    - "moderate deviations in channel coding"
    - "linear error correcting codes with anytime reliability"

Connected through hyperedge HE-8 (induced by source paper "uniform random number generation from markov chains non asymptotic and asymptotic analyses"):
  with members:
    - "security analysis of varepsilon almost dual universal 2 hash functions smoothing of min entropy versus smoothing of renyi entropy of order 2"
    - "second order coding rates for channels with state"
    - "non asymptotic and asymptotic analyses of information processing on markov chains"
    - "second order resolvability intrinsic randomness and fixed length source coding for mixed sources information spectrum approach"
    - "tight exponential analysis of universally composable privacy amplification and its applications"
    - "non asymptotic analysis of privacy amplification via renyi entropy and inf spectral entropy"
    - "exponential decreasing rate of leaked information in universal random privacy amplification"
    - "universal strongly secure network coding with dependent and non uniform messages"

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)