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: "learning to drive using inverse reinforcement learning and deep q networks"
  Abstract: We propose an inverse reinforcement learning (IRL) approach using Deep Q-Networks to extract the rewards in problems with large state spaces. We evaluate the performance of this approach in a simulation-based autonomous driving scenario. Our results resemble the intuitive relation between the reward function and readings of distance sensors mounted at different poses on the car. We also show that, after a few learning rounds, our simulated agent generates collision-free motions and performs human-like lane change behaviour.

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"
    - "combining deep reinforcement learning and safety based control for autonomous driving"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (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-4 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-5 (induced by source paper "parameter sharing reinforcement learning architecture for multi agent driving behaviors"):
  with members:
    - "a deep reinforcement learning approach for dynamically stable inverse kinematics of humanoid robots"
    - "counterfactual multi agent policy gradients"
    - "parameter sharing deep deterministic policy gradient for cooperative multi agent reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "asynchronous methods for deep reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "a centralized reinforcement learning method for multi agent job scheduling in grid"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving"):
  with members:
    - "virtual to real reinforcement learning for autonomous driving"
    - "autonomous braking system via deep reinforcement learning"
    - "learning from maps visual common sense for autonomous driving"
    - "benchmarking deep reinforcement learning for continuous control"
    - "an empirical evaluation of deep learning on highway driving"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "playing atari 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)