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: "an off policy policy gradient theorem using emphatic weightings"
  Abstract: Policy gradient methods are widely used for control in reinforcement learning, particularly for the continuous action setting. There have been a host of theoretically sound algorithms proposed for the on-policy setting, due to the existence of the policy gradient theorem which provides a simplified form for the gradient. In off-policy learning, however, where the behaviour policy is not necessarily attempting to learn and follow the optimal policy for the given task, the existence of such a theorem has been elusive. In this work, we solve this open problem by providing the first off-policy policy gradient theorem. The key to the derivation is the use of $emphatic$ $weightings$. We develop a new actor-critic algorithm$\unicode{x2014}$called Actor Critic with Emphatic weightings (ACE)$\unicode{x2014}$that approximates the simplified gradients provided by the theorem. We demonstrate in a simple counterexample that previous off-policy policy gradient methods$\unicode{x2014}$particularly OffPAC and DPG$\unicode{x2014}$converge to the wrong solution whereas ACE finds the optimal solution.

Connected through hyperedge HE-1 (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"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (induced by source paper "a nonparametric off policy policy gradient"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "off policy deep reinforcement learning without exploration"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "policy optimization via importance sampling"
    - "policy improvement for pomdps using normalized importance sampling"
    - "off policy actor critic"
    - "learning from scarce experience"

Connected through hyperedge HE-3 (induced by source paper "direct and indirect reinforcement learning"):
  with members:
    - "generalized off policy actor critic"
    - "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"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "provably convergent off policy actor critic with function approximation"):
  with members:
    - "minimax weight and q function learning for off policy evaluation"
    - "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"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-6 (induced by source paper "entropy regularization with discounted future state distribution in policy gradient methods"):
  with members:
    - "information asymmetry in kl regularized rl"
    - "the value function polytope in reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-7 (induced by source paper "off policy policy gradient with state distribution correction"):
  with members:
    - "generalized off policy actor critic"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "soft actor critic algorithms and applications"
    - "more robust doubly robust off policy evaluation"
    - "policy optimization via importance sampling"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "actor expert a framework for using q learning in continuous action spaces"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"

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)