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 the global convergence of actor critic a case for linear quadratic regulator with ergodic cost"
  Abstract: Despite the empirical success of the actor-critic algorithm, its theoretical understanding lags behind. In a broader context, actor-critic can be viewed as an online alternating update algorithm for bilevel optimization, whose convergence is known to be fragile. To understand the instability of actor-critic, we focus on its application to linear quadratic regulators, a simple yet fundamental setting of reinforcement learning. We establish a nonasymptotic convergence analysis of actor-critic in this setting. In particular, we prove that actor-critic finds a globally optimal pair of actor (policy) and critic (action-value function) at a linear rate of convergence. Our analysis may serve as a preliminary step towards a complete theoretical understanding of bilevel optimization with nonconvex subproblems, which is NP-hard in the worst case and is often solved using heuristics.

Connected through hyperedge HE-1 (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"
    - "neural temporal difference learning converges to global optima"
    - "global optimality guarantees for policy gradient methods"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-2 (induced by source paper "provably efficient exploration in policy optimization"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "exploration enhanced politex"
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "sqrt n regret for learning in markov decision processes with function approximation and low bellman rank"

Connected through hyperedge HE-3 (induced by source paper "convergent policy optimization for safe reinforcement learning"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "provably efficient q learning with low switching cost"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "stochastic successive convex approximation for non convex constrained stochastic optimization"
    - "learning safe policies with expert guidance"
    - "communication efficient distributed reinforcement learning"
    - "macro action selection with deep reinforcement learning in starcraft"

Connected through hyperedge HE-4 (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"
    - "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"
    - "fully decentralized multi agent reinforcement learning with networked agents"

Connected through hyperedge HE-5 (induced by source paper "analyzing the variance of policy gradient estimators for the linear quadratic regulator"):
  with members:
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "global convergence of policy gradient methods for linearized control problems"
    - "proximal policy optimization algorithms"
    - "benchmarking deep reinforcement learning for continuous control"
    - "trust region policy optimization"

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)