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 coding region for the discrete lossy gray wyner source coding problem"):
  Abstract: We derive the optimal second-order coding region for the lossy Gray-Wyner source coding problem for discrete memoryless sources under mild conditions. To do so, we leverage the properties of an appropriate generalization of the conditional distortion-tilted information density, which was first introduced by Kostina and Verd\'u (2012). The converse part uses the perturbation argument by Gu and Effros (2009) in their strong converse proof of the discrete Gray-Wyner problem. The achievability part uses a generalization of type covering lemmas and the uniform continuity of the conditional rate-distortion function in both the source joint distribution and the distortion level.

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 "strong successive refinability and rate distortion complexity tradeoff":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "the lossy common information of correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characterization of conditional independence and weak realizations of multivariate gaussian random variables applications to networks")
  Member "second order coding rates for channels with state":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "second order and moderate deviation analysis of a block fading channel with deterministic and energy harvesting power constraints")
  Member "a technique for deriving one shot achievability results in network information theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite sample analysis of image registration")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse regression codes")

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)