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: "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.

Connected through hyperedge HE-1 (induced by source paper "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"):
  with members:
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "variations on a theme by schalkwijk and kailath"
    - "achievable error exponents in the gaussian channel with rate limited feedback"
    - "feedback capacity of stationary gaussian channels"

Connected through hyperedge HE-2 (induced by source paper "an achievable rate region for the broadcast channel with feedback"):
  with members:
    - "on the capacity of the discrete memoryless broadcast channel with feedback"
    - "correlated sources over broadcast channels"
    - "comments on broadcast channels with arbitrarily correlated sources"
    - "approximate characterizations for the gaussian source broadcast distortion region"
    - "a new achievable rate region for the discrete memoryless multiple access channel with noiseless feedback"
    - "broadcasting correlated gaussians"
    - "a graph based framework for transmission of correlated sources over broadcast channels"

Connected through hyperedge HE-3 (induced by source paper "any positive feedback rate increases the capacity of strictly less noisy broadcast channels"):
  with members:
    - "an achievable rate region for the broadcast channel with feedback"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "on the capacity of the discrete memoryless broadcast channel with feedback"

Connected through hyperedge HE-4 (induced by source paper "approximate feedback capacity of the gaussian multicast channel"):
  with members:
    - "two way interference channels"
    - "wireless network information flow a deterministic approach"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "interference channels with rate limited feedback"
    - "feedback capacity of the gaussian interference channel to within 2 bits"
    - "noisy network coding"
    - "compound multiple access channels with partial cooperation"

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)