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: "dynamic policy programming"
  Abstract: In this paper, we propose a novel policy iteration method, called dynamic policy programming (DPP), to estimate the optimal policy in the infinite-horizon Markov decision processes. We prove the finite-iteration and asymptotic l\infty-norm performance-loss bounds for DPP in the presence of approximation/estimation error. The bounds are expressed in terms of the l\infty-norm of the average accumulated error as opposed to the l\infty-norm of the error in the case of the standard approximate value iteration (AVI) and the approximate policy iteration (API). This suggests that DPP can achieve a better performance than AVI and API since it averages out the simulation noise caused by Monte-Carlo sampling throughout the learning process. We examine this theoretical results numerically by com- paring the performance of the approximate variants of DPP with existing reinforcement learning (RL) methods on different problem domains. Our results show that, in all cases, DPP-based algorithms outperform other RL methods by a wide margin.

Connected through hyperedge HE-1 (induced by source paper "stable policy optimization via off policy divergence regularization"):
  with members:
    - "imitation learning via off policy distribution matching"
    - "deep conservative policy iteration"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "truly proximal policy optimization"
    - "a theory of regularized markov decision processes"
    - "algaedice policy gradient from arbitrary experience"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "on convergence and stability of gans"

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 "neural proximal trust region policy optimization attains globally optimal policy"):
  with members:
    - "sample optimal parametric q learning using linearly additive features"
    - "a theoretical analysis of deep q learning"
    - "optimality and approximation with policy gradient methods in markov decision processes"
    - "neural temporal difference learning converges to global optima"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "sample optimal parametric q learning with linear transition models"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-4 (induced by source paper "a theory of regularized markov decision processes"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic algorithms and applications"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "relative entropy regularized policy iteration"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "a short variational proof of equivalence between policy gradients and soft q learning"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-5 (induced by source paper "optimality and approximation with policy gradient methods in markov decision processes"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"
    - "global optimality guarantees for policy gradient methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "on oracle efficient pac rl with rich observations"
    - "understanding the impact of entropy on policy optimization"
    - "global convergence of policy gradient methods for linearized control problems"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning with dynamic boltzmann softmax updates"):
  with members:
    - "meta gradient reinforcement learning"
    - "revisiting the softmax bellman operator new benefits and new perspective"
    - "learning to walk via deep reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "boltzmann exploration done right"
    - "reinforcement learning with deep energy based policies"
    - "pgq combining policy gradient and q learning"

Connected through hyperedge HE-7 (induced by source paper "a convergence result for regularized actor critic methods"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "proximal policy optimization algorithms"
    - "pgq combining policy gradient and q learning"
    - "taming the noise in reinforcement learning via soft updates"

Connected through hyperedge HE-8 (induced by source paper "stochastic convergence results for regularized actor critic methods"):
  with members:
    - "a theory of regularized markov decision processes"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "proximal policy optimization algorithms"
    - "pgq combining policy gradient and q learning"
    - "actor critic algorithms for learning nash equilibria in n player general sum games"

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)