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: "derivative free methods for policy optimization guarantees for linear quadratic systems"
  Abstract: We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback. We show that these methods provably converge to within any pre-specified tolerance of the optimal policy with a number of zero-order evaluations that is an explicit polynomial of the error tolerance, dimension, and curvature properties of the problem. Our analysis reveals some interesting differences between the settings of additive driving noise and random initialization, as well as the settings of one-point and two-point reward feedback. Our theory is corroborated by extensive simulations of derivative-free methods on these systems. Along the way, we derive convergence rates for stochastic zero-order optimization algorithms when applied to a certain class of non-convex problems.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "on the convergence proof of amsgrad and a new version"
    - "smoothing policies and safe policy gradients"
    - "finite sample analysis for sarsa with linear function approximation"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"

Connected through hyperedge HE-2 (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"
    - "near optimal finite time identification of arbitrary linear dynamical 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-3 (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"
    - "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-4 (induced by source paper "neural policy gradient methods global optimality and rates of convergence"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "a theoretical analysis of deep q learning"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"

Connected through hyperedge HE-5 (induced by source paper "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"):
  with members:
    - "on gradient based learning in continuous games"
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "a theoretical analysis of deep q learning"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "local saddle point optimization a curvature exploitation approach"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

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"
    - "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"
    - "fully decentralized multi agent reinforcement learning with networked agents"

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"
    - "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"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

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"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "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"
    - "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.LG(Machine Learning)