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 "a source channel separation theorem with application to the source broadcast problem"):
  Abstract: A converse method is developed for the source broadcast problem. Specifically, it is shown that the separation architecture is optimal for a variant of the source broadcast problem and the associated source-channel separation theorem can be leveraged, via a reduction argument, to establish a necessary condition for the original problem, which unifies several existing results in the literature. Somewhat surprisingly, this method, albeit based on the source-channel separation theorem, can be used to prove the optimality of non-separation based schemes and determine the performance limits in certain scenarios where the separation architecture is suboptimal.

Includes members:
  Member "outer bounds on the admissible source region for broadcast channels with correlated sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  Member "broadcasting correlated vector gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vector gaussian successive refinement with degraded side information")
  Member "hybrid digital analog codes for source channel broadcast of gaussian sources over gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint state estimation and communication over a state dependent gaussian multiple access channel")
  Member "optimality and approximate optimality of source channel separation in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a new approach to lossy network compression of a tuple of correlated multivariate gaussian rvs")
  Member "approximate characterizations for the gaussian source broadcast distortion region":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distortion bounds for source broadcast over degraded channel")
  Member "broadcasting correlated gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "short packet downlink transmission with non orthogonal multiple access")
  Member "an extremal inequality motivated by multiterminal information theoretic problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vector gaussian successive refinement with degraded side information")
  Member "vector gaussian multiple description with individual and central receivers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vector gaussian successive refinement with degraded side information")

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)