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 braking system via deep reinforcement learning"
  Abstract: In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.

Connected through hyperedge HE-1 (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-2 (induced by source paper "safe reinforcement learning with scene decomposition for navigating complex urban environments"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "learning negotiating behavior between cars in intersections using deep q learning"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "deep variational reinforcement learning for pomdps"
    - "utility decomposition with deep corrections for scalable planning under uncertainty"
    - "belief state planning for autonomously navigating urban intersections"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-3 (induced by source paper "multi objective autonomous braking system using naturalistic dataset"):
  with members:
    - "analysis of distracted pedestrians waiting time head mounted immersive virtual reality application"
    - "unity a general platform for intelligent agents"
    - "virtual immersive reality for stated preference travel behaviour experiments a case study of autonomous vehicles on urban roads"
    - "multinet multi modal multi task learning for autonomous driving"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "proximal policy optimization algorithms"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "imminent collision mitigation with reinforcement learning and vision"):
  with members:
    - "world models"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "carla an open urban driving simulator"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "impact of ground truth annotation quality on performance of semantic image segmentation of traffic conditions"):
  with members:
    - "performance evaluation of deep learning networks for semantic segmentation of traffic stereo pair images"
    - "icnet for real time semantic segmentation on high resolution images"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "tensorflow a system for large scale machine learning"

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"
    - "learning from maps visual common sense for autonomous driving"
    - "learning to drive using inverse reinforcement learning and deep q networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "decomposition methods with deep corrections for reinforcement learning"):
  with members:
    - "evaluation of automated vehicles encountering pedestrians at unsignalized crossings"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "the complexity of decentralized control of markov decision processes"

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)