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 using feedback in a gaussian channel"
  Abstract: For information transmission a discrete time channel with independent additive Gaussian noise is used. There is also another channel with independent additive Gaussian noise (the feedback channel), and the transmitter observes without delay all outputs of the forward channel via that channel. Transmission of nonexponential number of messages is considered (i.e. transmission rate equals zero) and the achievable decoding error exponent for such a combination of channels is investigated. The transmission method strengthens the method used by authors earlier for BSC and Gaussian channels. In particular, for small feedback noise, it allows to gain 33.3\% (instead of 23.6\% earlier in the similar case of Gaussian channel).

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 awgn channels and gaussian macs with variable length 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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on gaussian macs with variable length feedback and non vanishing error probabilities"):
  with members:
    - "common message broadcast channels with feedback in the nonasymptotic regime full feedback"
    - "nonasymptotic coding rate bounds for binary erasure channels with feedback"
    - "a proof of the strong converse theorem for gaussian multiple access channels"
    - "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"
    - "broadcasting a common message with variable length stop feedback codes"
    - "on the dispersions of three network information theory problems"
    - "capacity and coding for the ising channel with feedback"

Connected through hyperedge HE-3 (induced by source paper "on awgn channels and gaussian macs with variable length feedback"):
  with members:
    - "nonasymptotic coding rate bounds for binary erasure channels with feedback"
    - "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"
    - "broadcasting a common message with variable length stop feedback codes"
    - "on the dispersions of three network information theory problems"
    - "capacity and coding for the ising channel with feedback"
    - "optimal feedback communication via posterior matching"
    - "error exponents for variable length block codes with feedback and cost constraints"

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)