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: "beyond the one step greedy approach in reinforcement learning"
  Abstract: The famous Policy Iteration algorithm alternates between policy improvement and policy evaluation. Implementations of this algorithm with several variants of the latter evaluation stage, e.g, $n$-step and trace-based returns, have been analyzed in previous works. However, the case of multiple-step lookahead policy improvement, despite the recent increase in empirical evidence of its strength, has to our knowledge not been carefully analyzed yet. In this work, we introduce the first such analysis. Namely, we formulate variants of multiple-step policy improvement, derive new algorithms using these definitions and prove their convergence. Moreover, we show that recent prominent Reinforcement Learning algorithms are, in fact, instances of our framework. We thus shed light on their empirical success and give a recipe for deriving new algorithms for future study.

Connected through hyperedge HE-1 (induced by source paper "trajectory wise control variates for variance reduction in policy gradient methods"):
  with members:
    - "policy optimization with stochastic mirror descent"
    - "contrasting exploration in parameter and action space a zeroth order optimization perspective"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "reward estimation variance elimination in sequential decision processes"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "predictor corrector policy optimization"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-2 (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"
    - "dual policy iteration"
    - "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-3 (induced by source paper "deep policies for width based planning in pixel domains"):
  with members:
    - "planning with pixels in almost real time"
    - "improving width based planning with compact policies"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "unifying count based exploration and intrinsic motivation"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "exploration conscious reinforcement learning revisited"):
  with members:
    - "smoothed action value functions for learning gaussian policies"
    - "distributed prioritized experience replay"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "noisy networks for exploration"
    - "bridging the gap between value and policy based reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "multiple step greedy policies in online and approximate reinforcement learning"):
  with members:
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning from the hindsight plan episodic mpc improvement"
    - "trust region policy optimization"
    - "approximate policy iteration schemes a comparison"

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)