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: "dual policy iteration"
  Abstract: Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at test time, and a slow, non-reactive policy (e.g., Tree Search), that can plan multiple steps ahead. The reactive policy is updated under supervision from the non-reactive policy, while the non-reactive policy is improved with guidance from the reactive policy. In this work we study this Dual Policy Iteration (DPI) strategy in an alternating optimization framework and provide a convergence analysis that extends existing API theory. We also develop a special instance of this framework which reduces the update of non-reactive policies to model-based optimal control using learned local models, and provides a theoretically sound way of unifying model-free and model-based RL approaches with unknown dynamics. We demonstrate the efficacy of our approach on various continuous control Markov Decision Processes.

Connected through hyperedge HE-1 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-2 (induced by source paper "sample efficient model free reinforcement learning with off policy critics"):
  with members:
    - "exploration by random network distillation"
    - "information directed exploration for deep reinforcement 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"
    - "rudder return decomposition for delayed rewards"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-3 (induced by source paper "when to trust your model model based policy optimization"):
  with members:
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "model ensemble trust region policy optimization"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "lipschitz continuity in model based reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"

Connected through hyperedge HE-4 (induced by source paper "on policy robot imitation learning from a converging supervisor"):
  with members:
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "model ensemble trust region policy optimization"
    - "addressing function approximation error in actor critic methods"
    - "convergence of value aggregation for imitation learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-5 (induced by source paper "imitation projected policy gradient for programmatic reinforcement learning"):
  with members:
    - "control regularization for reduced variance reinforcement learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "houdini lifelong learning as program synthesis"
    - "safe exploration in continuous action spaces"
    - "predictor corrector policy optimization"
    - "programmatically interpretable reinforcement learning"
    - "accelerating imitation learning with predictive models"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-6 (induced by source paper "multi step greedy policies in model free deep reinforcement learning"):
  with members:
    - "when to trust your model model based policy optimization"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "beyond the one step greedy approach in reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "exploration conscious reinforcement learning revisited"
    - "self correcting models for model based reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "interactive teaching algorithms for inverse reinforcement learning"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "understanding the role of adaptivity in machine teaching the case of version space learners"
    - "teaching inverse reinforcement learners via features and demonstrations"
    - "an overview of machine teaching"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "repeated inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "first person activity forecasting with online inverse reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "lipschitz continuity in model based reinforcement 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.LG(Machine Learning)