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: "the information flow and capacity of channels with noisy feedback"
  Abstract: In this paper, we consider some long-standing problems in communication systems with access to noisy feedback. We introduce a new notion, the residual directed information, to capture the effective information flow (i.e. mutual information between the message and the channel outputs) in the forward channel. In light of this new concept, we investigate discrete memoryless channels (DMC) with noisy feedback and prove that the noisy feedback capacity is not achievable by using any typical closed-loop encoder (non-trivially taking feedback information to produce channel inputs). We then show that the residual directed information can be used to characterize the capacity of channels with noisy feedback. Finally, we provide computable bounds on the noisy feedback capacity, which are characterized by the causal conditional directed information.

Connected through hyperedge HE-1 (induced by source paper "fundamental inequalities and identities involving mutual and directed informations in closed loop systems"):
  with members:
    - "data processing inequalities based on a certain structured class of information measures with application to estimation theory"
    - "a new data processing inequality and its applications in distributed source and channel coding"
    - "improved upper bounds to the causal quadratic rate distortion function for gaussian stationary sources"
    - "interpretations of directed information in portfolio theory data compression and hypothesis testing"
    - "a coding theorem for a class of stationary channels with feedback"
    - "achieving the gaussian rate distortion function by prediction"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-2 (induced by source paper "upper bound on the capacity of gaussian channels with noisy feedback"):
  with members:
    - "bounds on the achievable rate of noisy feedback gaussian channels under linear feedback coding scheme"
    - "concatenated coding for the awgn channel with noisy 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)