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 "feedback can double the prelog of some memoryless gaussian networks"):
  Abstract: We exhibit two memoryless Gaussian networks where the capacity-gains afforded by feedback are unbounded in the signal-to-noise ratio (SNR). The networks are instances of the Gaussian broadcast channel and the two-user Gaussian interference channel. To demonstrate the capacity-gains we propose and analyze a novel feedback coding scheme. For the broadcast channel with two receivers it is shown that if the noise sequences at the two receivers are perfectly anticorrelated, then, at high SNR, feedback asymptotically doubles the sum-capacity. The same holds if the noise sequences are perfectly correlated provided that they are of unequal variances. This result extends to the multi-receiver broadcast channel: if the noise sequences are all different and have a rank-one covariance matrix, then, at high-SNR, feedback asymptotically multiplies the sum-capacity by the number of receivers. However, as we show, these multiplicative gains collapse when the feedback is noisy. For the two-receiver Gaussian broadcast channel with noise-free feedback we also derive the high-SNR asymptotic sum-capacity. The expansion is exact in the sense that, as the SNR tends to infinity, the difference between the sum-capacity and our asymptotic expression tends to zero. If the noise sequences are perfectly anticorrelated or if they are perfectly correlated and of unequal variances, then the asymptotic expansion is as if the transmitter communicated to the two receivers over two parallel Gaussian channels. Otherwise, the asymptotic expansion is the same as if the receivers could cooperate. For the two-user interference channel it is shown that if the noises experienced by the two receivers are perfectly correlated or perfectly anticorrelated, then for most channel-gains feedback doubles the high SNR sum-capacity.

Includes members:
  Member "lqg control approach to gaussian broadcast channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "youla coding and computation of gaussian feedback capacity")
  Member "interference channels with rate limited feedback":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity results for intermittent x channels with delayed channel state feedback")
  Member "feedback capacity of the gaussian interference channel to within 2 bits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity limits of full duplex cellular network")
  Member "on the capacity of the discrete memoryless broadcast channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting location information to enhance throughput in downlink v2i systems")
  Member "on the gaussian mac with imperfect feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sum rate capacity for symmetric gaussian multiple access channels with feedback")
  Member "the pre log of gaussian broadcast with feedback can be two":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "posterior matching for gaussian broadcast channels with feedback")
  Member "on cognitive interference networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "degrees of freedom of interference networks with transmitter side caches")
  Member "the multiplexing gain of mimo x channels with partial transmit side information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "degrees of freedom of interference channels with comp transmission and reception")

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)