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: "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
  Abstract: Trust region policy optimization (TRPO) is a popular and empirically successful policy search algorithm in Reinforcement Learning (RL) in which a surrogate problem, that restricts consecutive policies to be `close' to one another, is iteratively solved. Nevertheless, TRPO has been considered a heuristic algorithm inspired by Conservative Policy Iteration (CPI). We show that the adaptive scaling mechanism used in TRPO is in fact the natural "RL version" of traditional trust-region methods from convex analysis. We first analyze TRPO in the planning setting, in which we have access to the model and the entire state space. Then, we consider sample-based TRPO and establish $\tilde O(1/\sqrt{N})$ convergence rate to the global optimum. Importantly, the adaptive scaling mechanism allows us to analyze TRPO in {\em regularized MDPs} for which we prove fast rates of $\tilde O(1/N)$, much like results in convex optimization. This is the first result in RL of better rates when regularizing the instantaneous cost or reward.

Connected through hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "information theoretic considerations in batch reinforcement learning"
    - "gradient aware model based policy search"
    - "global optimality guarantees for policy gradient methods"
    - "model ensemble trust region policy optimization"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "equivalence between wasserstein and value aware model based reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "optimistic policy optimization with bandit feedback"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "model free reinforcement learning in infinite horizon average reward markov decision processes"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "on connections between constrained optimization and reinforcement learning"
    - "provably efficient exploration in policy optimization"

Connected through hyperedge HE-3 (induced by source paper "a general framework for learning mean field games"):
  with members:
    - "q learning for mean field controls"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "a theory of regularized markov decision processes"
    - "multi agent reinforcement learning a report on challenges and approaches"
    - "real time bidding by reinforcement learning in display advertising"
    - "reinforcement learning with deep energy based policies"
    - "a unified view of entropy regularized markov decision processes"
    - "markov nash equilibria in mean field games with discounted cost"

Connected through hyperedge HE-4 (induced by source paper "on connections between constrained optimization and reinforcement learning"):
  with members:
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "deep conservative policy iteration"
    - "a theory of regularized markov decision processes"
    - "a unified view of entropy regularized markov decision processes"
    - "is the bellman residual a bad proxy"
    - "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)