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 driving in reality with reinforcement learning and image translation"
  Abstract: Supervised learning is widely used in training autonomous driving vehicle. However, it is trained with large amount of supervised labeled data. Reinforcement learning can be trained without abundant labeled data, but we cannot train it in reality because it would involve many unpredictable accidents. Nevertheless, training an agent with good performance in virtual environment is relatively much easier. Because of the huge difference between virtual and real, how to fill the gap between virtual and real is challenging. In this paper, we proposed a novel framework of reinforcement learning with image semantic segmentation network to make the whole model adaptable to reality. The agent is trained in TORCS, a car racing simulator.

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 "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "model free deep reinforcement learning for urban autonomous driving"
    - "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"
    - "model based reinforcement learning for atari"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "the limits and potentials of deep learning for robotics"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-3 (induced by source paper "latent space reinforcement learning for steering angle prediction"):
  with members:
    - "modular vehicle control for transferring semantic information to unseen weather conditions using gans"
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end learning for self driving cars"
    - "continuous control with deep reinforcement learning"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-4 (induced by source paper "social behavior learning with realistic reward shaping"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning to navigate in complex environments"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "learning socially appropriate robot approaching behavior toward groups using deep reinforcement learning"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "unity a general platform for intelligent agents"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "deep reinforcement learning that matters"
    - "robot gains social intelligence through multimodal deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "learning to navigate in complex environments"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "robust multimodal image registration using deep recurrent reinforcement learning"):
  with members:
    - "deep reinforcement learning based image captioning with embedding reward"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a survey on deep learning in medical image analysis"
    - "end to end active object tracking via reinforcement learning"
    - "an artificial agent for robust image registration"
    - "a deep metric for multimodal registration"
    - "asynchronous methods for deep reinforcement learning"
    - "fast predictive image registration"

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.CV(Computer Vision and Pattern Recognition)