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: "least squares temporal difference learning for the linear quadratic regulator"
  Abstract: Reinforcement learning (RL) has been successfully used to solve many continuous control tasks. Despite its impressive results however, fundamental questions regarding the sample complexity of RL on continuous problems remain open. We study the performance of RL in this setting by considering the behavior of the Least-Squares Temporal Difference (LSTD) estimator on the classic Linear Quadratic Regulator (LQR) problem from optimal control. We give the first finite-time analysis of the number of samples needed to estimate the value function for a fixed static state-feedback policy to within $\varepsilon$-relative error. In the process of deriving our result, we give a general characterization for when the minimum eigenvalue of the empirical covariance matrix formed along the sample path of a fast-mixing stochastic process concentrates above zero, extending a result by Koltchinskii and Mendelson in the independent covariates setting. Finally, we provide experimental evidence indicating that our analysis correctly captures the qualitative behavior of LSTD on several LQR instances.

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"
    - "finite time analysis of optimal adaptive policies for linear quadratic systems"
    - "learning unknown markov decision processes a thompson sampling approach"

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 "transfer reinforcement learning under unobserved contextual information"):
  with members:
    - "q learning with ucb exploration is sample efficient for infinite horizon mdp"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "importance weighted transfer of samples in reinforcement learning"
    - "successor features for transfer in reinforcement learning"
    - "deep recurrent q learning for partially observable mdps"
    - "transfer from multiple mdps"

Connected through hyperedge HE-4 (induced by source paper "neural temporal difference learning converges to global optima"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "td or not td analyzing the role of temporal differencing in deep reinforcement learning"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-5 (induced by source paper "sample complexity of estimating the policy gradient for nearly deterministic dynamical systems"):
  with members:
    - "contrasting exploration in parameter and action space a zeroth order optimization perspective"
    - "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"
    - "learning dexterous in hand manipulation"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "residual reinforcement learning for robot control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "is q learning provably efficient"

Connected through hyperedge HE-6 (induced by source paper "natural actor critic converges globally for hierarchical linear quadratic regulator"):
  with members:
    - "deep reinforcement learning for event driven multi agent decision processes"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "mean field multi agent reinforcement learning"
    - "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-7 (induced by source paper "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "finite sample analysis of the gtd policy evaluation algorithms in markov setting"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-8 (induced by source paper "on the global convergence of imitation learning a case for linear quadratic regulator"):
  with members:
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "global convergence of policy gradient methods for linearized control problems"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "boosting the actor with dual critic"

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)