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 the ozarow leung scheme for the gaussian broadcast channel with feedback"
  Abstract: In this work, we consider linear-feedback schemes for the two-user Gaussian broadcast channel with noiseless feedback. We extend the transmission scheme of [Ozarow and Leung, 1984] by applying estimators with memory instead of the memoryless estimators used by Ozarow and Leung (OL) in their original work. A recursive formulation of the mean square errors achieved by the proposed estimators is provided, along with a proof for the existence of a fixed point. This enables characterizing the achievable rates of the extended scheme. Finally, via numerical simulations it is shown that the extended scheme can improve upon the original OL scheme in terms of achievable rates, as well as achieve a low probability of error after a finite number of channel uses.

Connected through hyperedge HE-1 (induced by source paper "on the energy distortion tradeoff of gaussian broadcast channels with feedback"):
  with members:
    - "interactive schemes for the awgn channel with noisy 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-2 (induced by source paper "mimo mac bc duality with linear feedback coding schemes"):
  with members:
    - "the awgn bc with mac feedback a reduction to noiseless feedback via interaction"
    - "on the noisy feedback capacity of gaussian broadcast channels"
    - "insufficiency of linear feedback schemes in gaussian broadcast channels with common message"
    - "an achievable rate region for the broadcast channel with feedback"
    - "any positive feedback rate increases the capacity of strictly less noisy broadcast channels"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "on the capacity of the discrete memoryless broadcast channel with 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)