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: "upper bound on the capacity of gaussian channels with noisy feedback"
  Abstract: We consider an additive Gaussian channel with additive Gaussian noise feedback. We derive an upper bound on the n-block capacity (defined by Cover [1]). It is shown that this upper bound can be obtained by solving a convex optimization problem. With stationarity assumptions on Gaussian noise processes, we characterize the limit of the n-block upper bound and prove that this limit is the upper bound of the noisy feedback (shannon) capacity.

Connected through hyperedge HE-1 (induced by source paper "youla coding and computation of gaussian feedback capacity"):
  with members:
    - "control approach to computing the feedback capacity for stationary finite dimensional gaussian channels"
    - "bounds on the achievable rate of noisy feedback gaussian channels under linear feedback coding scheme"
    - "lqg control approach to gaussian broadcast channels with feedback"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "optimal feedback communication via posterior matching"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the feedback capacity of power constrained gaussian noise channels with memory"
    - "a coding theorem for a class of stationary channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "control approach to computing the feedback capacity for stationary finite dimensional gaussian channels"):
  with members:
    - "bounds on the achievable rate of noisy feedback gaussian channels under linear feedback coding scheme"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "optimal feedback communication via posterior matching"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"
    - "on the feedback capacity of power constrained gaussian noise channels with memory"
    - "a coding theorem for a class of stationary channels with feedback"
    - "feedback capacity of stationary gaussian channels"
    - "the capacity of channels 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)