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: "on awgn channels and gaussian macs with variable length feedback"
  Abstract: We characterize the information-theoretic limits of the additive white Gaussian noise (AWGN) channel and the Gaussian multiple access channel (MAC) when variable-length feedback is available at the encoder and a non-vanishing error probability is permitted. For the AWGN channel, we establish the $\varepsilon$-capacity (for $0<\varepsilon<1$) and show that it is larger than the corresponding $\varepsilon$-capacity when fixed-length feedback is available. Due to the continuous nature of the channel and the presence of expected power constraints, we need to develop new achievability and converse techniques. In addition, we show that a variable-length feedback with termination (VLFT) code outperforms a stop-feedback code in terms of the second-order asymptotic behavior. Finally, we extend out analyses to the Gaussian MAC with the two types of variable-length feedback where we establish the $\varepsilon$-capacity region. Due to the multi-terminal nature of the channel model, we are faced with the need to bound the asymptotic behavior of the expected value of the maximum of several stopping times. We do so by leveraging tools from renewal theory developed by Lai and Siegmund.

Connected through hyperedge HE-1 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback"):
  with members:
    - "common message broadcast channels with feedback in the nonasymptotic regime full feedback"
    - "on gaussian macs with variable length feedback and non vanishing error probabilities"
    - "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime"
    - "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities"
    - "on using feedback in a gaussian channel"
    - "broadcasting a common message with variable length stop feedback codes"
    - "opportunistic capacity and error exponent regions for compound channel with feedback"
    - "error exponents for the gaussian channel with active noisy feedback"

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)