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: "the dispersion of joint source channel coding"
  Abstract: In this work we investigate the behavior of the distortion threshold that can be guaranteed in joint source-channel coding, to within a prescribed excess-distortion probability. We show that the gap between this threshold and the optimal average distortion is governed by a constant that we call the joint source-channel dispersion. This constant can be easily computed, since it is the sum of the source and channel dispersions, previously derived. The resulting performance is shown to be better than that of any separation-based scheme. For the proof, we use unequal error protection channel coding, thus we also evaluate the dispersion of that setting.

Connected through hyperedge HE-1 (induced by source paper "a lower bound on the expected distortion of joint source channel coding"):
  with members:
    - "sequential empirical coordination under an output entropy constraint"
    - "a source channel separation theorem with application to the source broadcast problem"
    - "outer bounds on the admissible source region for broadcast channels with correlated sources"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "quasi static multiple antenna fading channels at finite blocklength"
    - "moderate deviations in channel coding"
    - "lossy joint source channel coding in the finite blocklength regime"

Connected through hyperedge HE-2 (induced by source paper "the dispersion of the gauss markov source"):
  with members:
    - "second order region for gray wyner network"
    - "on the dispersions of three network information theory problems"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "a note on rate distortion functions for nonstationary gaussian autoregressive processes"

Connected through hyperedge HE-3 (induced by source paper "new converse bounds for discrete memoryless channels in the finite blocklength regime"):
  with members:
    - "lossy joint source channel coding in the finite blocklength regime"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "information spectrum approach to second order coding rate in channel coding"

Connected through hyperedge HE-4 (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"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "second order analysis for joint source channel coding with markovian source"):
  with members:
    - "uniform random number generation from markov chains non asymptotic and asymptotic analyses"
    - "non asymptotic and asymptotic analyses of information processing on markov chains"
    - "finite length analyses for source and channel coding on markov chains"
    - "lossy joint source channel coding in the finite blocklength regime"
    - "information spectrum approach to second order coding rate in channel coding"
    - "second order asymptotics in fixed length source coding and intrinsic randomness"

Connected through hyperedge HE-6 (induced by source paper "finite length bounds for joint source channel coding with markovian source and additive channel noise to achieve large and moderate deviation bounds"):
  with members:
    - "non asymptotic and asymptotic analyses of information processing on markov chains"
    - "lossy joint source channel coding in the finite blocklength regime"

Connected through hyperedge HE-7 (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-8 (induced by source paper "multi class source channel coding"):
  with members:
    - "unequal message protection asymptotic and non asymptotic tradeoffs"
    - "a derivation of the source channel error exponent using non identical product distributions"
    - "lossy joint source channel coding in the finite blocklength regime"

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)