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: "a unified view of entropy regularized markov decision processes"
  Abstract: We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes (MDPs). Our approach is based on extending the linear-programming formulation of policy optimization in MDPs to accommodate convex regularization functions. Our key result is showing that using the conditional entropy of the joint state-action distributions as regularization yields a dual optimization problem closely resembling the Bellman optimality equations. This result enables us to formalize a number of state-of-the-art entropy-regularized reinforcement learning algorithms as approximate variants of Mirror Descent or Dual Averaging, and thus to argue about the convergence properties of these methods. In particular, we show that the exact version of the TRPO algorithm of Schulman et al. (2015) actually converges to the optimal policy, while the entropy-regularized policy gradient methods of Mnih et al. (2016) may fail to converge to a fixed point. Finally, we illustrate empirically the effects of using various regularization techniques on learning performance in a simple reinforcement learning setup.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning via fenchel rockafellar duality"):
  with members:
    - "double reinforcement learning for efficient off policy evaluation in markov decision processes"
    - "minimax weight and q function learning for off policy evaluation"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "algaedice policy gradient from arbitrary experience"
    - "a divergence minimization perspective on imitation learning methods"
    - "imitation learning as f divergence minimization"

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"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "reverse curriculum generation for reinforcement learning"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"

Connected through hyperedge HE-5 (induced by source paper "a general framework for learning mean field games"):
  with members:
    - "q learning for mean field controls"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "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"
    - "markov nash equilibria in mean field games with discounted cost"

Connected through hyperedge HE-6 (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-7 (induced by source paper "algaedice policy gradient from arbitrary experience"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "off policy policy gradient with state distribution correction"
    - "doubly robust bias reduction in infinite horizon off policy estimation"
    - "imitation learning via off policy distribution matching"
    - "multi agent manipulation via locomotion using hierarchical sim2real"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "a theory of regularized markov decision processes"
    - "a kernel loss for solving the bellman equation"

Connected through hyperedge HE-8 (induced by source paper "a unified bellman optimality principle combining reward maximization and empowerment"):
  with members:
    - "mutual information regularization in markov decision processes and actor critic learning"
    - "empowerment driven exploration using mutual information estimation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "a unified strategy for implementing curiosity and empowerment driven reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

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)