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: "convergent actor critic algorithms under off policy training and function approximation"
  Abstract: We present the first class of policy-gradient algorithms that work with both state-value and policy function-approximation, and are guaranteed to converge under off-policy training. Our solution targets problems in reinforcement learning where the action representation adds to the-curse-of-dimensionality; that is, with continuous or large action sets, thus making it infeasible to estimate state-action value functions (Q functions). Using state-value functions helps to lift the curse and as a result naturally turn our policy-gradient solution into classical Actor-Critic architecture whose Actor uses state-value function for the update. Our algorithms, Gradient Actor-Critic and Emphatic Actor-Critic, are derived based on the exact gradient of averaged state-value function objective and thus are guaranteed to converge to its optimal solution, while maintaining all the desirable properties of classical Actor-Critic methods with no additional hyper-parameters. To our knowledge, this is the first time that convergent off-policy learning methods have been extended to classical Actor-Critic methods with function approximation.

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"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "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"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-4 (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"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "a multi agent off policy actor critic algorithm for distributed reinforcement learning"):
  with members:
    - "distributed off policy actor critic reinforcement learning with policy consensus"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "safe and efficient off policy reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "two time scale off policy td learning non asymptotic analysis over markovian samples"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "on convergence of some gradient based temporal differences algorithms for off policy learning"
    - "stochastic recursive inclusions in two timescales with non additive iterate dependent markov noise"

Connected through hyperedge HE-7 (induced by source paper "actor expert a framework for using action value methods in continuous action spaces"):
  with members:
    - "discrete sequential prediction of continuous actions for deep rl"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"

Connected through hyperedge HE-8 (induced by source paper "an off policy policy gradient theorem using emphatic weightings"):
  with members:
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "sample efficient actor critic with experience replay"
    - "unifying task specification in reinforcement learning"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "an emphatic approach to the problem of off policy temporal difference learning"
    - "on convergence of emphatic temporal difference learning"

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)