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: "combining deep reinforcement learning and safety based control for autonomous driving"
  Abstract: With the development of state-of-art deep reinforcement learning, we can efficiently tackle continuous control problems. But the deep reinforcement learning method for continuous control is based on historical data, which would make unpredicted decisions in unfamiliar scenarios. Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance. In this passage, we use the Deep Deterministic Policy Gradient algorithm to implement autonomous driving without vehicles around. The vehicle can learn the driving policy in a stable and familiar environment, which is efficient and reliable. Then we use the artificial potential field to design collision avoidance algorithm with vehicles around. The path tracking method is also taken into consideration. The combination of deep reinforcement learning and safety based control performs well in most scenarios.

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 "learning to drive via apprenticeship learning and deep reinforcement learning"):
  with members:
    - "learning unmanned aerial vehicle control for autonomous target following"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "end to end learning for self driving cars"
    - "generative adversarial imitation learning"
    - "learning to drive using inverse reinforcement learning and deep q networks"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "v2i connectivity based dynamic queue jumper lane for emergency vehicles an approximate dynamic programming approach"):
  with members:
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "prioritized experience replay"

Connected through hyperedge HE-4 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-5 (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-6 (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"
    - "composing meta policies for autonomous driving using hierarchical deep reinforcement learning"
    - "learning sampling distributions for robot motion planning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"

Connected through hyperedge HE-7 (induced by source paper "centralized conflict free cooperation for connected and automated vehicles at intersections by proximal policy optimization"):
  with members:
    - "end to end race driving with deep reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "model based value estimation for efficient model free reinforcement learning"
    - "learning to drive in a day"
    - "carla an open urban driving simulator"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "dynamic weights in multi objective deep reinforcement learning"):
  with members:
    - "universal successor representations for transfer reinforcement learning"
    - "selective experience replay for lifelong learning"
    - "successor features for transfer in reinforcement learning"
    - "multi objective deep reinforcement learning"
    - "prioritized experience replay"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q 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.RO(Robotics)