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: "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
  Abstract: Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This paper reports results from experiments using Deep Reinforcement Learning on a continuous-state, discrete-action autonomous driving simulator. We explore how Deep Neural Networks can represent meta-policies that switch among a set of previously learned policies, specifically in settings where the dynamics of a new scenario are composed of a mixture of previously learned dynamics and where the state observation is possibly corrupted by sensing noise. We also report the results of experiments varying dynamics mixes, distractor policies, magnitudes/distributions of sensing noise, and obstacles. In a fully observed experiment, the meta-policy learning algorithm achieves 2.6x the reward achieved by the next best policy composition technique with 80% less exploration. In a partially observed experiment, the meta-policy learning algorithm converges after 50 iterations while a direct application of RL fails to converge even after 200 iterations.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "exploring applications of deep reinforcement learning for real world autonomous driving systems"):
  with members:
    - "on first order meta learning algorithms"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "hierarchical imitation and reinforcement learning"
    - "learning to drive in a day"

Connected through hyperedge HE-3 (induced by source paper "design space of behaviour planning for autonomous driving"):
  with members:
    - "learning on road visual control for self driving vehicles with auxiliary tasks"
    - "world models"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "environment independent task specifications via gltl"
    - "learning sampling distributions for robot motion planning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-4 (induced by source paper "option compatible reward inverse reinforcement learning"):
  with members:
    - "multi level discovery of deep options"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"
    - "hirl hierarchical inverse reinforcement learning for long horizon tasks with delayed rewards"
    - "the option critic architecture"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "unsupervised perceptual rewards for imitation learning"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-5 (induced by source paper "learning on road visual control for self driving vehicles with auxiliary tasks"):
  with members:
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "end to end learning for self driving cars"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "end to end learning of driving models from large scale video datasets"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "simulated autonomous driving in a realistic driving environment using deep reinforcement learning and a deterministic finite state machine"):
  with members:
    - "simulated autonomous driving on realistic road networks using deep reinforcement learning"
    - "combining deep reinforcement learning and safety based control for autonomous driving"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "continuous control with deep reinforcement learning"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "learning end to end multimodal sensor policies for autonomous navigation"):
  with members:
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "sensor fusion for robot control through deep reinforcement learning"
    - "blockout dynamic model selection for hierarchical deep networks"
    - "end to end learning for self driving cars"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "continuous deep q learning with model based acceleration"
    - "zoneout regularizing rnns by randomly preserving hidden activations"

Connected through hyperedge HE-8 (induced by source paper "multi level discovery of deep options"):
  with members:
    - "learning to repeat fine grained action repetition for deep reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "option discovery in hierarchical reinforcement learning using spatio temporal clustering"
    - "learning from the memory of atari 2600"
    - "learning and transfer of modulated locomotor controllers"
    - "the option critic architecture"
    - "hierarchical linearly solvable markov decision problems"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

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)