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: "learning linear dynamical systems via spectral filtering"
  Abstract: We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for convexity of the loss functions. From this arises a polynomial-time algorithm with a near-optimal regret guarantee, with an analogous sample complexity bound for agnostic learning. Our algorithm is based on a novel filtering technique, which may be of independent interest: we convolve the time series with the eigenvectors of a certain Hankel matrix.

Connected through hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems"):
  with members:
    - "regret minimization in partially observable linear quadratic control"
    - "improper learning for non stochastic control"
    - "learning linear dynamical systems with semi parametric least squares"
    - "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"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"

Connected through hyperedge HE-2 (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"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "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"

Connected through hyperedge HE-3 (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"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-4 (induced by source paper "logarithmic regret for learning linear quadratic regulators efficiently"):
  with members:
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "regret minimization in partially observable linear quadratic control"):
  with members:
    - "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"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-6 (induced by source paper "the nonstochastic control problem"):
  with members:
    - "learning linear dynamical systems with semi parametric least squares"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "logarithmic regret for online control"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "global convergence of policy gradient methods for 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"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "boosting for dynamical systems"):
  with members:
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "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"
    - "global convergence of policy gradient methods for linearized control problems"
    - "gradient descent learns linear dynamical systems"

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.LG(Machine Learning)