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 "joint source channel coding with time varying channel and side information"):
  Abstract: Transmission of a Gaussian source over a time-varying Gaussian channel is studied in the presence of time-varying correlated side information at the receiver. A block fading model is considered for both the channel and the side information, whose states are assumed to be known only at the receiver. The optimality of separate source and channel coding in terms of average end-to-end distortion is shown when the channel is static while the side information state follows a discrete or a continuous and quasiconcave distribution. When both the channel and side information states are time-varying, separate source and channel coding is suboptimal in general. A partially informed encoder lower bound is studied by providing the channel state information to the encoder. Several achievable transmission schemes are proposed based on uncoded transmission, separate source and channel coding, joint decoding as well as hybrid digital-analog transmission. Uncoded transmission is shown to be optimal for a class of continuous and quasiconcave side information state distributions, while the channel gain may have an arbitrary distribution. To the best of our knowledge, this is the first example in which the uncoded transmission achieves the optimal performance thanks to the time-varying nature of the states, while it is suboptimal in the static version of the same problem. Then, the optimal \emph{distortion exponent}, that quantifies the exponential decay rate of the expected distortion in the high SNR regime, is characterized for Nakagami distributed channel and side information states, and it is shown to be achieved by hybrid digital-analog and joint decoding schemes in certain cases, illustrating the suboptimality of pure digital or analog transmission in general.

Includes members:
  Member "minimum expected distortion in gaussian source coding with fading side information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust baseband compression against congestion in packet based fronthaul networks using multiple description coding")
  Member "joint source channel coding with correlated interference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new and more general capacity theorem for the gaussian channel with two sided input noise dependent state information")
  Member "wyner ziv coding over broadcast channels digital schemes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "noisy broadcast networks with receiver caching")
  Member "distortion minimization in gaussian layered broadcast coding with successive refinement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "layered coding for energy harvesting communication without csit")
  Member "joint source channel coding with side information using hybrid digital analog codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimum energy analysis for robust gaussian joint source channel coding with a square law profile")
  Member "on the distortion snr exponent of some layered transmission schemes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non parametric message important measure storage code design and transmission planning for big data")
  Member "joint source channel codes for mimo block fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback")

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)