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 "broadcasting correlated gaussians"):
  Abstract: We consider the transmission of a memoryless bivariate Gaussian source over an average-power-constrained one-to-two Gaussian broadcast channel. The transmitter observes the source and describes it to the two receivers by means of an average-power-constrained signal. Each receiver observes the transmitted signal corrupted by a different additive white Gaussian noise and wishes to estimate the source component intended for it. That is, Receiver~1 wishes to estimate the first source component and Receiver~2 wishes to estimate the second source component. Our interest is in the pairs of expected squared-error distortions that are simultaneously achievable at the two receivers. #R##N#We prove that an uncoded transmission scheme that sends a linear combination of the source components achieves the optimal power-versus-distortion trade-off whenever the signal-to-noise ratio is below a certain threshold. The threshold is a function of the source correlation and the distortion at the receiver with the weaker noise.

Includes members:
  Member "the achievable distortion region of bivariate gaussian source on gaussian broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distortion bounds for source broadcast over degraded channel")
  Member "hybrid coding for gaussian broadcast channels with gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed source simulation with no communication")
  Member "sending a bivariate gaussian source over a gaussian mac with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels with feedback")
  Member "sending a bi variate gaussian source over a gaussian mac":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels with feedback")
  Member "sending a bi variate gaussian source over a gaussian mac":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels 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)