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 driving maneuvers under interactive environment based on deep reinforcement learning"
  Abstract: Safe and efficient autonomous driving maneuvers in an interactive and complex environment can be considerably challenging due to the unpredictable actions of other surrounding agents that may be cooperative or adversarial in their interactions with the ego vehicle. One of the state-of-the-art approaches is to apply Reinforcement Learning (RL) to learn a time-sequential driving policy, to execute proper control strategy or tracking trajectory in dynamic situations. However, direct application of RL algorithms is not satisfactorily enough to deal with the cases in the autonomous driving domain, mainly due to the complex driving environment and continuous action space. In this paper, we adopt Q-learning as our basic learning framework and design a unique format of the Q-function approximator that consists of neural networks to handle the continuous action space challenge. The learning model is present in a closed form of continuous control variables and trained in a simulation platform that we have developed with embedded properties of real-time vehicle interactions. The proposed algorithm avoids invoking an additional actor network that learns to take actions, as in actor-critic algorithms. At the same time, some prior knowledge of vehicle dynamics is also fed into the model to assist learning. We test our algorithm with a challenging use case - lane change maneuver, to verify the practicability and feasibility of the proposed approach. Results from accumulated rewards and vehicle performance show that RL vehicle agents successfully learn a safe, comfort and efficient driving policy as defined in the reward function.

Connected through hyperedge HE-1 (induced by source paper "adversarial inverse reinforcement learning for decision making in autonomous driving"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "imitating driver behavior with generative adversarial networks"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "a connection between generative adversarial networks inverse reinforcement learning and energy based models"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "human like decision making for autonomous driving via adversarial inverse reinforcement learning"):
  with members:
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "imitating driver behavior with generative adversarial networks"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generative adversarial imitation learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "continuous control with deep reinforcement learning"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (induced by source paper "a data driven method of optimizing feedforward compensator for autonomous vehicle"):
  with members:
    - "self driving cars a survey"
    - "a reinforcement learning based approach for automated lane change maneuvers"

Connected through hyperedge HE-4 (induced by source paper "a data driven method of feedforward compensator optimization for autonomous vehicle control"):
  with members:
    - "self driving cars a survey"
    - "a reinforcement learning based approach for automated lane change maneuvers"

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)