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 optimality of adaptive linear quadratic regulators"
  Abstract: Adaptive regulation of linear systems represents a canonical problem in stochastic control. Performance of adaptive control policies is assessed through the regret with respect to the optimal regulator, that reflects the increase in the operating cost due to uncertainty about the parameters that drive the dynamics of the system. However, available results in the literature do not provide a sharp quantitative characterization of the effect of the unknown dynamics parameters on the regret. Further, there are issues on how easy it is to implement the adaptive policies proposed in the literature. Finally, results regarding the accuracy that the system's parameters are identified are scarce and rather incomplete. #R##N#This study aims to comprehensively address these three issues. First, by introducing a novel decomposition of adaptive policies, we establish a sharp expression for the regret of an arbitrary policy in terms of the deviations from the optimal regulator. Second, we show that adaptive policies based on a slight modification of the widely used Certainty Equivalence scheme are optimal. Specifically, we establish a regret of (nearly) square-root rate for two families of randomized adaptive policies. The presented regret bounds are obtained by using anti-concentration results on the random matrices employed when randomizing the estimates of the unknown dynamics parameters. Moreover, we study the minimal additional information needed on dynamics matrices for which the regret will become of logarithmic order. Finally, the rate at which the unknown parameters of the system are being identified is specified for the proposed adaptive policies.

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"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "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 "logarithmic regret for adversarial online control"):
  with members:
    - "logarithmic regret for learning linear quadratic regulators efficiently"
    - "improper learning for non stochastic control"
    - "naive exploration is optimal for online lqr"
    - "online convex optimization in adversarial markov decision processes"
    - "introduction to online convex optimization"
    - "online control with adversarial disturbances"
    - "logarithmic regret for online control"
    - "the nonstochastic control problem"

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"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "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 "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"
    - "garbage in reward out bootstrapping exploration in multi armed bandits"
    - "new insights into bootstrapping for bandits"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "finite time identification in unstable linear systems"
    - "regret analysis for adaptive linear quadratic policies"

Connected through hyperedge HE-5 (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"
    - "an information matrix approach for state secrecy"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "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)