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: "trust pcl an off policy trust region method for continuous control"
  Abstract: Trust region methods, such as TRPO, are often used to stabilize policy optimization algorithms in reinforcement learning (RL). While current trust region strategies are effective for continuous control, they typically require a prohibitively large amount of on-policy interaction with the environment. To address this problem, we propose an off-policy trust region method, Trust-PCL. The algorithm is the result of observing that the optimal policy and state values of a maximum reward objective with a relative-entropy regularizer satisfy a set of multi-step pathwise consistencies along any path. Thus, Trust-PCL is able to maintain optimization stability while exploiting off-policy data to improve sample efficiency. When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.

Connected through hyperedge HE-1 (induced by source paper "off policy deep reinforcement learning with analogous disentangled exploration"):
  with members:
    - "mulex disentangling exploitation from exploration in deep rl"
    - "importance resampling for off policy prediction"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"

Connected through hyperedge HE-2 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation"):
  with members:
    - "gradientdice rethinking generalized offline estimation of stationary values"
    - "a nonparametric offpolicy policy gradient"
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "dualdice behavior agnostic estimation of discounted stationary distribution corrections"
    - "algaedice policy gradient from arbitrary experience"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-3 (induced by source paper "continuous action reinforcement learning for playing racing games comparing spg to ppo"):
  with members:
    - "combining experience replay with exploration by random network distillation"
    - "sampled policy gradient for learning to play the game agar io"
    - "proximal policy optimization algorithms"
    - "continuous deep q learning with model based acceleration"
    - "prioritized experience replay"
    - "trust region policy optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "direct and indirect reinforcement learning"):
  with members:
    - "generalized off policy actor critic"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model ensemble trust region policy optimization"
    - "self imitation learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-6 (induced by source paper "generalized off policy actor critic"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "online off policy prediction"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-7 (induced by source paper "hindsight trust region policy optimization"):
  with members:
    - "hierarchical reinforcement learning with hindsight"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "visual reinforcement learning with imagined goals"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hierarchical imitation and reinforcement learning"
    - "many goals reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "information asymmetry in kl regularized rl"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "kickstarting deep reinforcement learning"
    - "mix match agent curricula for reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "deep mutual learning"
    - "fixing a broken elbo"
    - "a unified bellman equation for causal information and value in markov decision processes"

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.AI(Artificial Intelligence)