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 feedback capacity of power constrained gaussian noise channels with memory"
  Abstract: For a stationary additive Gaussian-noise channel with a rational noise power spectrum of a finite-order $L$, we derive two new results for the feedback capacity under an average channel input power constraint. First, we show that a very simple feedback-dependent Gauss-Markov source achieves the feedback capacity, and that Kalman-Bucy filtering is optimal for processing the feedback. Based on these results, we develop a new method for optimizing the channel inputs for achieving the Cover-Pombra block-length-$n$ feedback capacity by using a dynamic programming approach that decomposes the computation into $n$ sequentially identical optimization problems where each stage involves optimizing $O(L^2)$ variables. Second, we derive the explicit maximal information rate for stationary feedback-dependent sources. In general, evaluating the maximal information rate for stationary sources requires solving only a few equations by simple non-linear programming. For first-order autoregressive and/or moving average (ARMA) noise channels, this optimization admits a closed form maximal information rate formula. The maximal information rate for stationary sources is a lower bound on the feedback capacity, and it equals the feedback capacity if the long-standing conjecture, that stationary sources achieve the feedback capacity, holds.

Connected through hyperedge HE-1 (induced by source paper "capacity of continuous channels with memory via directed information neural estimator"):
  with members:
    - "computing the feedback capacity of finite state channels using reinforcement learning"
    - "conditional mutual information neural estimator"
    - "deep learning for channel coding via neural mutual information estimation"
    - "neural entropic estimation a faster path to mutual information estimation"
    - "itene intrinsic transfer entropy neural estimator"
    - "mine mutual information neural estimation"
    - "estimating the directed information and testing for causality"
    - "universal estimation of directed information"

Connected through hyperedge HE-2 (induced by source paper "a connection between feedback capacity and kalman filter for colored gaussian noises"):
  with members:
    - "generic variance bounds on estimation and prediction errors in time series analysis an entropy perspective"
    - "youla coding and computation of gaussian feedback capacity"
    - "feedback capacity of stationary gaussian channels further examined"
    - "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"
    - "feedback capacity of gaussian channels revisited"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity achieving feedback schemes for gaussian finite state markov channels with channel state information"
    - "convergence of fundamental limitations in feedback communication estimation and feedback control over gaussian channels"

Connected through hyperedge HE-3 (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"
    - "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"
    - "a coding theorem for a class of stationary channels with feedback"

Connected through hyperedge HE-4 (induced by source paper "feedback capacity of stationary gaussian channels further examined"):
  with members:
    - "feedback capacity of stationary gaussian channels"
    - "feedback capacity of the first order moving average gaussian channel"

Connected through hyperedge HE-5 (induced by source paper "sequential necessary and sufficient conditions for capacity achieving distributions of channels with memory and feedback"):
  with members:
    - "capacity achieving distributions information lossless randomized strategies for feedback channels with memory the lqg theory of directed information part ii"
    - "a single letter upper bound on the feedback capacity of unifilar finite state channels"
    - "information structures of capacity achieving distributions for feedback channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of binary state symmetric channel with and without feedback and transmission cost"
    - "information structures for feedback capacity of channels with memory and transmission cost stochastic optimal control variational equalities part i"
    - "capacity of a post channel with and without feedback"
    - "directed information on abstract spaces properties and variational equalities"
    - "directed information on abstract spaces properties and extremum problems"

Connected through hyperedge HE-6 (induced by source paper "a single letter upper bound on the feedback capacity of unifilar finite state channels"):
  with members:
    - "distributed channel synthesis"
    - "entropy bounds for discrete random variables via maximal coupling"
    - "capacity and coding for the ising channel with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-7 (induced by source paper "control approach to computing the feedback capacity for stationary finite dimensional gaussian channels"):
  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"
    - "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"
    - "a coding theorem for a class of stationary channels with feedback"
    - "feedback capacity of stationary gaussian channels"
    - "the capacity of channels with feedback"

Connected through hyperedge HE-8 (induced by source paper "upper bounds on the capacities of noncontrollable finite state channels with without feedback"):
  with members:
    - "optimization of information rate upper and lower bounds for channels with memory"
    - "a coding theorem for a class of stationary channels with feedback"
    - "finite state channels with time invariant deterministic feedback"
    - "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)