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: "finite time analysis of optimal adaptive policies for linear quadratic systems"
  Abstract: We consider the classical problem of control of linear systems with quadratic cost. When the true system dynamics are unknown, an adaptive policy is required for learning the model parameters and planning a control policy simultaneously. Addressing this trade-off between accurate estimation and good control represents the main challenge in the area of adaptive control. Another important issue is to prevent the system becoming destabilized due to lack of knowledge of its dynamics. Asymptotically optimal approaches have been extensively studied in the literature, but there are very few non-asymptotic results which also do not provide a comprehensive treatment of the problem. #R##N#In this work, we establish finite time high probability regret bounds that are optimal up to logarithmic factors. We also provide high probability guarantees for a stabilization algorithm based on random linear feedbacks. The results are obtained under very mild assumptions, requiring: (i) stabilizability of the matrices encoding the system's dynamics, and (ii) degree of heaviness of the noise distribution. To derive our results, we also introduce a number of new concepts and technical tools.

Connected through hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "on applications of bootstrap in continuous space reinforcement learning"
    - "on optimality of adaptive linear quadratic regulators"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "learning unknown markov decision processes a thompson sampling approach"

Connected through hyperedge HE-2 (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-3 (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"
    - "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 "on applications of bootstrap in continuous space reinforcement learning"):
  with members:
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "on optimality of adaptive linear quadratic regulators"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "finite time identification in unstable linear systems"
    - "regret analysis for adaptive linear quadratic policies"

Connected through hyperedge HE-7 (induced by source paper "learning linear quadratic regulators efficiently with only t regret"):
  with members:
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "online linear quadratic control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "learning based control of unknown linear systems with thompson sampling"

Connected through hyperedge HE-8 (induced by source paper "randomized algorithms for data driven stabilization of stochastic linear systems"):
  with members:
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "on optimality of adaptive linear quadratic regulators"
    - "an information matrix approach for state secrecy"
    - "finite time identification in unstable linear 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.SY(Systems and Control)