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: "strong successive refinability and rate distortion complexity tradeoff"
  Abstract: We investigate the second order asymptotics (source dispersion) of the successive refinement problem. Similarly to the classical definition of a successively refinable source, we say that a source is strongly successively refinable if successive refinement coding can achieve the second order optimum rate (including the dispersion terms) at both decoders. We establish a sufficient condition for strong successive refinability. We show that any discrete source under Hamming distortion and the Gaussian source under quadratic distortion are strongly successively refinable. #R##N#We also demonstrate how successive refinement ideas can be used in point-to-point lossy compression problems in order to reduce complexity. We give two examples, the binary-Hamming and Gaussian-quadratic cases, in which a layered code construction results in a low complexity scheme that attains optimal performance. For example, when the number of layers grows with the block length $n$, we show how to design an $O(n^{\log(n)})$ algorithm that asymptotically achieves the rate-distortion bound.

Connected through hyperedge HE-1 (induced by source paper "exponential strong converse for successive refinement with causal decoder side information"):
  with members:
    - "strong converse using change of measure arguments"
    - "exponential strong converse for content identification with lossy recovery"
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "new strong converse for asymmetric broadcast channels"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "exponent function for one helper source coding problem at rates outside the rate region"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "on cooperative multiple access channels with delayed csi at transmitters"

Connected through hyperedge HE-2 (induced by source paper "second order region for gray wyner network"):
  with members:
    - "discrete lossy gray wyner revisited second order asymptotics large and moderate deviations"
    - "interactive communication for data exchange"
    - "second order asymptotics for the gaussian mac with degraded message sets"
    - "asymptotic estimates in information theory with non vanishing error probabilities"
    - "information complexity density and simulation of protocols"
    - "strong converse and second order asymptotics of channel resolvability"
    - "channels with cost constraints strong converse and dispersion"
    - "secret key agreement general capacity and second order asymptotics"

Connected through hyperedge HE-3 (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"
    - "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"
    - "moderate deviations in channel coding"

Connected through hyperedge HE-4 (induced by source paper "successive refinement of abstract sources"):
  with members:
    - "data compression with low distortion and finite blocklength"
    - "fixed length lossy compression in the finite blocklength regime"

Connected through hyperedge HE-5 (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"
    - "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"
    - "moderate deviations in channel coding"

Connected through hyperedge HE-6 (induced by source paper "second order coding region for the discrete lossy gray wyner source coding problem"):
  with members:
    - "second order region for gray wyner network"
    - "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"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"

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)