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: "recurrent deterministic policy gradient method for bipedal locomotion on rough terrain challenge"
  Abstract: This paper presents a deep learning framework that is capable of solving partially observable locomotion tasks based on our novel interpretation of Recurrent Deterministic Policy Gradient (RDPG). We study on bias of sampled error measure and its variance induced by the partial observability of environment and subtrajectory sampling, respectively. Three major improvements are introduced in our RDPG based learning framework: tail-step bootstrap of interpolated temporal difference, initialisation of hidden state using past trajectory scanning, and injection of external experiences learned by other agents. The proposed learning framework was implemented to solve the Bipedal-Walker challenge in OpenAI's gym simulation environment where only partial state information is available. Our simulation study shows that the autonomous behaviors generated by the RDPG agent are highly adaptive to a variety of obstacles and enables the agent to effectively traverse rugged terrains for long distance with higher success rate than leading contenders.

Connected through hyperedge HE-1 (induced by source paper "soft q network"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic algorithms and applications"
    - "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"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "high efficiency rl agent"):
  with members:
    - "benchmarking model based reinforcement learning"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "world models"
    - "soft actor critic algorithms and applications"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "on policy reinforcement learning with entropy regularization"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "world models"
    - "deep reinforcement learning that matters"
    - "proximal policy optimization algorithms"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "bipedal walking robot using deep deterministic policy gradient"):
  with members:
    - "continuous control with deep reinforcement learning"
    - "playing atari with deep 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.AI(Artificial Intelligence)