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: "control approach to computing the feedback capacity for stationary finite dimensional gaussian channels"
  Abstract: We firstly extend the interpretation of feedback communication over stationary finite dimensional Gaussian channels as feedback control systems by showing that, the problem of finding stabilizing feedback controllers with maximal reliable transmission rate over Youla parameters coincides with the problem of finding strictly causal filters to achieve feedback capacity recently derived in [1]. The aforementioned interpretation provides an approach to construct deterministic feedback coding schemes (with double exponential decaying error probability). We next propose an asymptotic capacity-achieving upper bounds, which can be numerically evaluated by solving finite dimensional dual optimizations. From the filters that achieve upper bounds, we derive feasible filters which lead to a sequence of lower bounds. Thus, from the lower bound filters we obtain communication systems that achieve the lower bound rate. Extensive examples show the sequence of lower bounds is asymptotic capacity-achieving as well.

Connected through hyperedge HE-1 (induced by source paper "youla coding and computation of gaussian feedback capacity"):
  with members:
    - "upper bound on the capacity of gaussian channels with noisy feedback"
    - "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 "secrecy capacity of colored gaussian noise channels with feedback"):
  with members:
    - "multiple access wiretap channel with noiseless feedback"
    - "an improved feedback coding scheme for the wire tap channel"
    - "secrecy degrees of freedom of mimo broadcast channels with delayed csit"
    - "the wiretap channel with feedback encryption over the channel"
    - "secret communication with feedback"
    - "secrecy capacity of the wiretap channel with noisy feedback"
    - "feedback capacity of stationary gaussian channels"

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)