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: "end to end race driving with deep reinforcement learning"
  Abstract: We present research using the latest reinforcement learning algorithm for end-to-end driving without any mediated perception (object recognition, scene understanding). The newly proposed reward and learning strategies lead together to faster convergence and more robust driving using only RGB image from a forward facing camera. An Asynchronous Actor Critic (A3C) framework is used to learn the car control in a physically and graphically realistic rally game, with the agents evolving simultaneously on tracks with a variety of road structures (turns, hills), graphics (seasons, location) and physics (road adherence). A thorough evaluation is conducted and generalization is proven on unseen tracks and using legal speed limits. Open loop tests on real sequences of images show some domain adaption capability of our method.

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 "overview of tools supporting planning for automated driving"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "self driving cars a survey"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "design space of behaviour planning for autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"

Connected through hyperedge HE-3 (induced by source paper "end to end model free reinforcement learning for urban driving using implicit affordances"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "is deep reinforcement learning really superhuman on atari leveling the playing field"
    - "learning by cheating"
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "conditional affordance learning for driving in urban environments"
    - "end to end vehicle lateral control using a single fisheye camera"

Connected through hyperedge HE-4 (induced by source paper "improved exploration through latent trajectory optimization in deep deterministic policy gradient"):
  with members:
    - "universal planning networks"
    - "deepmind control suite"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning to drive in a day"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"

Connected through hyperedge HE-5 (induced by source paper "neurotrajectory a neuroevolutionary approach to local state trajectory learning for autonomous vehicles"):
  with members:
    - "deep grid net dgn a deep learning system for real time driving context understanding"
    - "deep imitative models for flexible inference planning and control"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"
    - "an overview of multi task learning in deep neural networks"
    - "watch this scalable cost function learning for path planning in urban environments"

Connected through hyperedge HE-6 (induced by source paper "dynamic input for deep reinforcement learning in autonomous driving"):
  with members:
    - "on the limitations of representing functions on sets"
    - "deep sets"
    - "addressing function approximation error in actor critic methods"
    - "adaptive behavior generation for autonomous driving using deep reinforcement learning with compact semantic states"
    - "zero shot sim to real transfer with modular priors"
    - "proximal policy optimization algorithms"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "end to end training of deep visuomotor policies"

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:
    - "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"
    - "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-8 (induced by source paper "deep sensor fusion for real time odometry estimation"):
  with members:
    - "an lstm network for real time odometry estimation"
    - "an lstm network for highway trajectory prediction"
    - "coupled longitudinal and lateral control of a vehicle using deep learning"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

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)