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: "pegasus a policy search method for large mdps and pomdps"
  Abstract: We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which all state transitions (given the current state and action) are deterministic. This reduces the general problem of policy search to one in which we need only consider POMDPs with deterministic transitions. We give a natural way of estimating the value of all policies in these transformed POMDPs. Policy search is then simply performed by searching for a policy with high estimated value. We also establish conditions under which our value estimates will be good, recovering theoretical results similar to those of Kearns, Mansour and Ng (1999), but with "sample complexity" bounds that have only a polynomial rather than exponential dependence on the horizon time. Our method applies to arbitrary POMDPs, including ones with infinite state and action spaces. We also present empirical results for our approach on a small discrete problem, and on a complex continuous state/continuous action problem involving learning to ride a bicycle.

Connected through hyperedge HE-1 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control"):
  with members:
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "guided evolutionary strategies augmenting random search with surrogate gradients"
    - "deep reinforcement learning that matters"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-2 (induced by source paper "influence aware memory for deep reinforcement learning"):
  with members:
    - "a sufficient statistic for influence in structured multiagent environments"
    - "attention is all you need"
    - "proximal policy optimization algorithms"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep attention recurrent q network"
    - "deep recurrent q learning for partially observable mdps"
    - "the arcade learning environment an evaluation platform for general agents"
    - "the divergence of reinforcement learning algorithms with value iteration and function approximation"

Connected through hyperedge HE-3 (induced by source paper "on value functions and the agent environment boundary"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "information theoretic considerations in batch reinforcement learning"
    - "on oracle efficient pac rl with rich observations"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "contextual decision processes with low bellman rank are pac learnable"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "pipps flexible model based policy search robust to the curse of chaos"):
  with members:
    - "black box data efficient policy search for robotics"
    - "variational inference for monte carlo objectives"
    - "gradient estimation using stochastic computation graphs"

Connected through hyperedge HE-5 (induced by source paper "a survey on policy search algorithms for learning robot controllers in a handful of trials"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "reset free trial and error learning for robot damage recovery"
    - "an algorithmic perspective on imitation learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "world models"
    - "sim to real learning agile locomotion for quadruped robots"
    - "state representation learning for control an overview"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for page wise recommendations"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "reinforcement mechanism design for e commerce"
    - "deep reinforcement learning with attention for slate markov decision processes with high dimensional states and actions"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "policy optimization via importance sampling"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "towards generalization and simplicity in continuous control"
    - "using options and covariance testing for long horizon off policy policy evaluation"
    - "proximal policy optimization algorithms"
    - "sample efficient actor critic with experience replay"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-8 (induced by source paper "recommendations with negative feedback via pairwise deep reinforcement learning"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deep reinforcement learning for page wise recommendations"
    - "deep reinforcement learning for list wise recommendations"
    - "deep reinforcement learning with attention for slate markov decision processes with high dimensional states and actions"
    - "session based recommendations with recurrent neural networks"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "playing atari with deep reinforcement learning"
    - "off policy actor critic"

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)