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: "separation of source network coding and channel coding in wireline networks"
  Abstract: In this paper we prove the separation of source-network coding and channel coding in wireline networks. For the purposes of this work, a wireline network is any network of independent, memoryless, point-to-point, finite-alphabet channels used to transmit dependent sources either losslessly or subject to a distortion constraint. In deriving this result, we also prove that in a general memoryless network with dependent sources, lossless and zero-distortion reconstruction are equivalent provided that the conditional entropy of each source given the other sources is non-zero. Furthermore, we extend the separation result to the case of continuous-alphabet, point-to-point channels such as additive white Gaussian noise (AWGN) channels.

Connected through hyperedge HE-1 (induced by source paper "scalable capacity bounding models for wireless networks"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "a generalized typicality for abstract alphabets"
    - "distributed channel synthesis"
    - "capacity of multiple unicast in wireless networks a polymatroidal approach"
    - "wireless network information flow a deterministic approach"
    - "path gain algebraic formulation for the scalar linear network coding problem"
    - "optimality and approximate optimality of source channel separation in networks"
    - "a theory of network equivalence parts i and ii"

Connected through hyperedge HE-2 (induced by source paper "constrained source coding with side information"):
  with members:
    - "interactive function computation with reconstruction constraints"
    - "heegard berger and cascade source coding problems with common reconstruction constraints"
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "duality between channel capacity and rate distortion with two sided state information"

Connected through hyperedge HE-3 (induced by source paper "interactive function computation with reconstruction constraints"):
  with members:
    - "channel simulation via interactive communications"

Connected through hyperedge HE-4 (induced by source paper "on capacity region of wiretap networks"):
  with members:
    - "network equivalence in the presence of an eavesdropper"
    - "network coding capacity regions via entropy functions"
    - "on the separation of lossy source network coding and channel coding in wireline networks"

Connected through hyperedge HE-5 (induced by source paper "optimality and approximate optimality of source channel separation in networks"):
  with members:
    - "on the separation of lossy source network coding and channel coding in wireline networks"
    - "cascade and triangular source coding with side information at the first two nodes"
    - "cascade multiterminal source coding"
    - "hybrid coding for gaussian broadcast channels with gaussian sources"
    - "approximate characterizations for the gaussian source broadcast distortion region"
    - "multicasting correlated multi source to multi sink over a network"
    - "on multistage successive refinement for wyner ziv source coding with degraded side informations"
    - "coding into a source a direct inverse rate distortion theorem"

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)