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: "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
  Abstract: The effectiveness of model-based versus model-free methods is a long-standing question in reinforcement learning (RL). Motivated by recent empirical success of RL on continuous control tasks, we study the sample complexity of popular model-based and model-free algorithms on the Linear Quadratic Regulator (LQR). We show that for policy evaluation, a simple model-based plugin method requires asymptotically less samples than the classical least-squares temporal difference (LSTD) estimator to reach the same quality of solution; the sample complexity gap between the two methods can be at least a factor of state dimension. For policy evaluation, we study a simple family of problem instances and show that nominal (certainty equivalence principle) control also requires a factor of state dimension fewer samples than the policy gradient method to reach the same level of control performance on these instances. Furthermore, the gap persists even when employing baselines commonly used in practice. To the best of our knowledge, this is the first theoretical result which demonstrates a separation in the sample complexity between model-based and model-free methods on a continuous control task.

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 "observational overfitting in reinforcement learning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "weight agnostic neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "simple random search provides a competitive approach to reinforcement learning"

Connected through hyperedge HE-3 (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-4 (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"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"

Connected through hyperedge HE-5 (induced by source paper "extending deep model predictive control with safety augmented value estimation from demonstrations"):
  with members:
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "sample based learning model predictive control for linear uncertain systems"
    - "playing hard exploration games by watching youtube"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "hindsight experience replay"

Connected through hyperedge HE-6 (induced by source paper "bootstrapping the expressivity with model based planning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "exploring model based planning with policy networks"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "fixup initialization residual learning without normalization"
    - "calibrated model based deep reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"

Connected through hyperedge HE-7 (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"
    - "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"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-8 (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"
    - "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"
    - "fully decentralized multi agent reinforcement learning with networked agents"

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)