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 lossy common information of correlated sources"
  Abstract: The two most prevalent notions of common information (CI) are due to Wyner and Gacs-Korner and both the notions can be stated as two different characteristic points in the lossless Gray-Wyner region. Although the information theoretic characterizations for these two CI quantities can be easily evaluated for random variables with infinite entropy (eg., continuous random variables), their operational significance is applicable only to the lossless framework. The primary objective of this paper is to generalize these two CI notions to the lossy Gray-Wyner network, which hence extends the theoretical foundation to general sources and distortion measures. We begin by deriving a single letter characterization for the lossy generalization of Wyner's CI, defined as the minimum rate on the shared branch of the Gray-Wyner network, maintaining minimum sum transmit rate when the two decoders reconstruct the sources subject to individual distortion constraints. To demonstrate its use, we compute the CI of bivariate Gaussian random variables for the entire regime of distortions. We then similarly generalize Gacs and Korner's definition to the lossy framework. The latter half of the paper focuses on studying the tradeoff between the total transmit rate and receive rate in the Gray-Wyner network. We show that this tradeoff yields a contour of points on the surface of the Gray-Wyner region, which passes through both the Wyner and Gacs-Korner operating points, and thereby provides a unified framework to understand the different notions of CI. We further show that this tradeoff generalizes the two notions of CI to the excess sum transmit rate and receive rate regimes, respectively.

Connected through hyperedge HE-1 (induced by source paper "characterization of conditional independence and weak realizations of multivariate gaussian random variables applications to networks"):
  with members:
    - "a new approach to lossy network compression of a tuple of correlated multivariate gaussian rvs"

Connected through hyperedge HE-2 (induced by source paper "a new approach to lossy network compression of a tuple of correlated multivariate gaussian rvs"):
  with members:
    - "optimality and approximate optimality of source channel separation in networks"

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"
    - "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"
    - "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 "a rate distortion approach to caching"):
  with members:
    - "second order region for gray wyner network"
    - "benefits of cache assignment on degraded broadcast channels"
    - "rate memory trade off for the two user broadcast caching network with correlated sources"
    - "noisy broadcast networks with receiver caching"
    - "centralized coded caching for heterogeneous lossy requests"
    - "content delivery in erasure broadcast channels with cache and feedback"
    - "coded caching for a large number of users"
    - "information theoretic caching sequential coding for computing"

Connected through hyperedge HE-5 (induced by source paper "extracting wyner s common information using polar codes and polar lattices"):
  with members:
    - "distributed simulation of continuous random variables"
    - "gaussian secure source coding and wyner s common information"
    - "polar lattices for lossy compression"
    - "construction of capacity achieving lattice codes polar lattices"
    - "semantically secure lattice codes for the gaussian wiretap channel"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "achieving the capacity of any dmc using only polar codes"
    - "how to construct polar codes"

Connected through hyperedge HE-6 (induced by source paper "extracting wyner s common randomness using polar codes"):
  with members:
    - "polar lattices are good for lossy compression"
    - "polar lattices for lossy compression"
    - "achieving awgn channel capacity with lattice gaussian coding"
    - "how to construct polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-7 (induced by source paper "gaussian secure source coding and wyner s common information"):
  with members:
    - "an extremal inequality for long markov chains"
    - "secure coordination with a two sided helper"
    - "wyner s common information generalizations and a new lossy source coding interpretation"
    - "a bit of secrecy for gaussian source compression"
    - "optimal equivocation in secrecy systems a special case of distortion based characterization"
    - "on secure source coding with side information at the encoder"
    - "distributed channel synthesis"
    - "rate distortion theory for secrecy systems"

Connected through hyperedge HE-8 (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"
    - "strong successive refinability and rate distortion complexity tradeoff"
    - "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)