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 "second order and moderate deviation asymptotics for successive refinement"):
  Abstract: We derive the optimal second-order coding region and moderate deviations constant for successive refinement source coding with a joint excess-distortion probability constraint. We consider two scenarios: (i) a discrete memoryless source (DMS) and arbitrary distortion measures at the decoders and (ii) a Gaussian memoryless source (GMS) and quadratic distortion measures at the decoders. For a DMS with arbitrary distortion measures, we prove an achievable second-order coding region, using type covering lemmas by Kanlis and Narayan and by No, Ingber and Weissman. We prove the converse using the perturbation approach by Gu and Effros. When the DMS is successively refinable, the expressions for the second-order coding region and the moderate deviations constant are simplified and easily computable. For this case, we also obtain new insights on the second-order behavior compared to the scenario where separate excess-distortion proabilities are considered. For example, we describe a DMS, for which the optimal second-order region transitions from being characterizable by a bivariate Gaussian to a univariate Gaussian, as the distortion levels are varied. We then consider a GMS with quadratic distortion measures. To prove the direct part, we make use of the sphere covering theorem by Verger-Gaugry, together with appropriately-defined Gaussian type classes. To prove the converse, we generalize Kostina and Verd\'u's one-shot converse bound for point-to-point lossy source coding. We remark that this proof is applicable to general successively refinable sources. In the proofs of the moderate deviations results for both scenarios, we follow a strategy similar to that for the second-order asymptotics and use the moderate deviations principle.

Includes members:
  Member "second order region for gray wyner network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the dispersion of the gauss markov source")
  Member "second order asymptotics for the gaussian mac with degraded message sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "second order region for gray wyner network")
  Member "strong successive refinability and rate distortion complexity tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "asymptotic estimates in information theory with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite sample analysis of image registration")
  Member "second order coding rates for channels with state":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "second order and moderate deviation analysis of a block fading channel with deterministic and energy harvesting power constraints")
  Member "a case where interference does not affect the channel dispersion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "second order region for gray wyner network")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "on the dispersions of the gel fand pinsker channel and dirty paper coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sharp bounds for mutual covering")

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)