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: "gaussian channel with noisy feedback and peak energy constraint"
  Abstract: Optimal coding over the additive white Gaussian noise channel under the peak energy constraint is studied when there is noisy feedback over an orthogonal additive white Gaussian noise channel. As shown by Pinsker, under the peak energy constraint, the best error exponent for communicating an M-ary message, M >= 3, with noise-free feedback is strictly larger than the one without feedback. This paper extends Pinsker's result and shows that if the noise power in the feedback link is sufficiently small, the best error exponent for conmmunicating an M-ary message can be strictly larger than the one without feedback. The proof involves two feedback coding schemes. One is motivated by a two-stage noisy feedback coding scheme of Burnashev and Yamamoto for binary symmetric channels, while the other is a linear noisy feedback coding scheme that extends Pinsker's noise-free feedback coding scheme. When the feedback noise power $\alpha$ is sufficiently small, the linear coding scheme outperforms the two-stage (nonlinear) coding scheme, and is asymptotically optimal as $\alpha$ tends to zero. By contrast, when $\alpha$ is relatively larger, the two-stage coding scheme performs better.

Connected through hyperedge HE-1 (induced by source paper "achievable error exponents of one way and two way awgn channels"):
  with members:
    - "the gaussian channel with noisy feedback improving reliability via interaction"
    - "error exponents for the gaussian channel with active noisy feedback"
    - "error exponents for variable length block codes with feedback and cost constraints"

Connected through hyperedge HE-2 (induced by source paper "interactive schemes for the awgn channel with noisy feedback"):
  with members:
    - "the gaussian channel with noisy feedback improving reliability via interaction"
    - "the gaussian channel with noisy feedback near capacity performance via simple interaction"
    - "variations on a theme by schalkwijk and kailath"
    - "error exponents for the gaussian channel with active noisy feedback"
    - "optimal feedback communication via posterior matching"
    - "concatenated coding for the awgn channel with noisy feedback"
    - "joint wyner ziv dirty paper coding by modulo lattice modulation"

Connected through hyperedge HE-3 (induced by source paper "achievable error exponents in the gaussian channel with rate limited feedback"):
  with members:
    - "on reliability function of gaussian channel with noisy feedback zero transmission rate"
    - "zero rate feedback can achieve the empirical capacity"
    - "variations on a theme by schalkwijk and kailath"
    - "error exponents for the gaussian channel with active noisy feedback"
    - "optimal feedback communication via posterior matching"

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)