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: "autonomous ramp merge maneuver based on reinforcement learning with continuous action space"
  Abstract: Ramp merging is a critical maneuver for road safety and traffic efficiency. Most of the current automated driving systems developed by multiple automobile manufacturers and suppliers are typically limited to restricted access freeways only. Extending the automated mode to ramp merging zones presents substantial challenges. One is that the automated vehicle needs to incorporate a future objective (e.g. a successful and smooth merge) and optimize a long-term reward that is impacted by subsequent actions when executing the current action. Furthermore, the merging process involves interaction between the merging vehicle and its surrounding vehicles whose behavior may be cooperative or adversarial, leading to distinct merging countermeasures that are crucial to successfully complete the merge. In place of the conventional rule-based approaches, we propose to apply reinforcement learning algorithm on the automated vehicle agent to find an optimal driving policy by maximizing the long-term reward in an interactive driving environment. Most importantly, in contrast to most reinforcement learning applications in which the action space is resolved as discrete, our approach treats the action space as well as the state space as continuous without incurring additional computational costs. Our unique contribution is the design of the Q-function approximation whose format is structured as a quadratic function, by which simple but effective neural networks are used to estimate its coefficients. The results obtained through the implementation of our training platform demonstrate that the vehicle agent is able to learn a safe, smooth and timely merging policy, indicating the effectiveness and practicality of our approach.

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 "longitudinal dynamic versus kinematic models for car following control using deep reinforcement learning"):
  with members:
    - "urban driving with multi objective deep reinforcement learning"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "adaptive behavior generation for autonomous driving using deep reinforcement learning with compact semantic states"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "distributed distributional deterministic policy gradients"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "learning values across many orders of magnitude"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "urban driving with multi objective deep reinforcement learning"):
  with members:
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "unifying count based exploration and intrinsic motivation"
    - "prioritized experience replay"
    - "continuous control 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)