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: "near optimal finite time identification of arbitrary linear dynamical systems"
  Abstract: We derive finite time error bounds for estimating general linear time-invariant (LTI) systems from a single observed trajectory using the method of least squares. We provide the first analysis of the general case when eigenvalues of the LTI system are arbitrarily distributed in three regimes: stable, marginally stable, and explosive. Our analysis yields sharp upper bounds for each of these cases separately. We observe that although the underlying process behaves quite differently in each of these three regimes, the systematic analysis of a self--normalized martingale difference term helps bound identification error up to logarithmic factors of the lower bound. On the other hand, we demonstrate that the least squares solution may be statistically inconsistent under certain conditions even when the signal-to-noise ratio is high.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems"):
  with members:
    - "finite time analysis of approximate policy iteration for the linear quadratic regulator"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning compact neural networks with regularization"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "online learning of the kalman filter with logarithmic regret"):
  with members:
    - "naive exploration is optimal for online lqr"
    - "learning linear dynamical systems with semi parametric least squares"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "spectral filtering for general linear dynamical systems"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-3 (induced by source paper "naive exploration is optimal for online lqr"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "learning linear dynamical systems with semi parametric least squares"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "logarithmic regret for online control"
    - "on optimality of adaptive linear quadratic regulators"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-4 (induced by source paper "no regret prediction in marginally stable systems"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "robust guarantees for learning an autoregressive filter"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-5 (induced by source paper "logarithmic regret for learning linear quadratic regulators efficiently"):
  with members:
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "spectral filtering for general linear dynamical systems"
    - "online linear quadratic control"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-6 (induced by source paper "active learning for identification of linear dynamical systems"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "sample complexity lower bounds for linear system identification"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "spectral filtering for general linear dynamical systems"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

Connected through hyperedge HE-7 (induced by source paper "learning linear dynamical systems with semi parametric least squares"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "finite time identification in unstable linear systems"
    - "learning linear dynamical systems via spectral filtering"

Connected through hyperedge HE-8 (induced by source paper "finite time analysis of approximate policy iteration for the linear quadratic regulator"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "model free linear quadratic control via reduction to expert prediction"

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.SY(Systems and Control)