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: "interactive schemes for the awgn channel with noisy feedback"
  Abstract: We study the problem of communication over an additive white Gaussian noise (AWGN) channel with an AWGN feedback channel. When the feedback channel is noiseless, the classic Schalkwijk-Kailath (S-K) scheme is known to achieve capacity in a simple sequential fashion, while attaining reliability superior to non-feedback schemes. In this work, we show how simplicity and reliability can be attained even when the feedback is noisy, provided that the feedback channel is sufficiently better than the feedforward channel. Specifically, we introduce a low-complexity low-delay interactive scheme that operates close to capacity for a fixed bit error probability (e.g. $10^{-6}$). We then build on this scheme to provide two asymptotic constructions, one based on high dimensional lattices, and the other based on concatenated coding, that admit an error exponent significantly exceeding the best possible non-feedback exponent. Our approach is based on the interpretation of feedback transmission as a side-information problem, and employs an interactive modulo-lattice solution.

Connected through hyperedge HE-1 (induced by source paper "feedback channel communication with low precision arithmetic"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "variations on a theme by schalkwijk and kailath"
    - "optimal feedback communication via posterior matching"
    - "concatenated coding for the awgn channel with noisy feedback"

Connected through hyperedge HE-2 (induced by source paper "deepcode feedback codes via deep learning"):
  with members:
    - "learning a code machine learning for approximate non linear coded computation"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "end to end learning of communications systems without a channel model"
    - "deep learning for joint source channel coding of text"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-3 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels with feedback"):
  with members:
    - "on the ozarow leung scheme for the gaussian broadcast channel with feedback"
    - "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"
    - "mimo mac bc duality with linear feedback coding schemes"
    - "source channel coding theorems for the multiple access relay channel"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "optimality and approximate optimality of source channel separation in networks"
    - "variations on a theme by schalkwijk and kailath"
    - "sending a bivariate gaussian source over a gaussian mac with feedback"

Connected through hyperedge HE-4 (induced by source paper "performance analysis and optimal filter design for sigma delta modulation via duality with dpcm"):
  with members:
    - "the gaussian channel with noisy feedback near capacity performance via simple interaction"
    - "precoded integer forcing universally achieves the mimo capacity to within a constant gap"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "achieving the gaussian rate distortion function by prediction"
    - "multiple description coding by dithered delta sigma quantization"

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)