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: "automated speed and lane change decision making using deep reinforcement learning"
  Abstract: This paper introduces a method, based on deep reinforcement learning, for automatically generating a general purpose decision making function. A Deep Q-Network agent was trained in a simulated environment to handle speed and lane change decisions for a truck-trailer combination. In a highway driving case, it is shown that the method produced an agent that matched or surpassed the performance of a commonly used reference model. To demonstrate the generality of the method, the exact same algorithm was also tested by training it for an overtaking case on a road with oncoming traffic. Furthermore, a novel way of applying a convolutional neural network to high level input that represents interchangeable objects is also introduced.

Connected through hyperedge HE-1 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "lane change decision making through deep reinforcement learning with rule based constraints"
    - "safe efficient and comfortable velocity control based on reinforcement learning for autonomous driving"
    - "autonomous highway driving using deep reinforcement learning"
    - "improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning"
    - "human like autonomous car following model with deep reinforcement learning"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "a deep multi agent reinforcement learning approach to autonomous separation assurance"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "starcraft ii a new challenge for reinforcement learning"
    - "social attention modeling attention in human crowds"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"

Connected through hyperedge HE-3 (induced by source paper "automated lane change strategy using proximal policy optimization based deep reinforcement learning"):
  with members:
    - "quadratic q network for learning continuous control for autonomous vehicles"
    - "continuous control for automated lane change behavior based on deep deterministic policy gradient algorithm"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "zero shot deep reinforcement learning driving policy transfer for autonomous vehicles based on robust control"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "intelligent roundabout insertion using deep reinforcement learning"):
  with members:
    - "an introduction to deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "carla an open urban driving simulator"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "end to end driving via conditional imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-5 (induced by source paper "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"):
  with members:
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "randomized adversarial imitation learning for autonomous driving"):
  with members:
    - "generative adversarial self imitation learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "deep reinforcement learning that matters"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "towards generalization and simplicity in continuous control"
    - "optiongan learning joint reward policy options using generative adversarial inverse reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "automated lane change decision making using deep reinforcement learning in dynamic and uncertain highway environment"):
  with members:
    - "end to end learning for self driving cars"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "learning when to drive in intersections by combining reinforcement learning and model predictive control"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "real time constrained trajectory planning and vehicle control for proactive autonomous driving with road users"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning values across many orders of magnitude"

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.RO(Robotics)