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 "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.

Includes members:
  Member "nonasymptotic coding rate bounds for binary erasure channels with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "common message broadcast channels with feedback in the nonasymptotic regime full feedback")
  Member "variable length coding with stop feedback for the common message broadcast channel in the nonasymptotic regime":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "on gaussian channels with feedback under expected power constraints and with non vanishing error probabilities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "on using feedback in a gaussian channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "broadcasting a common message with variable length stop feedback codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the reliability function of the common message broadcast channel with variable length feedback")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "capacity and coding for the ising channel with feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing the feedback capacity of finite state channels using reinforcement learning")
  Member "optimal feedback communication via posterior matching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "feedback channel communication with low precision arithmetic")

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)