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 in a day"
  Abstract: We demonstrate the first application of deep reinforcement learning to autonomous driving. From randomly initialised parameters, our model is able to learn a policy for lane following in a handful of training episodes using a single monocular image as input. We provide a general and easy to obtain reward: the distance travelled by the vehicle without the safety driver taking control. We use a continuous, model-free deep reinforcement learning algorithm, with all exploration and optimisation performed on-vehicle. This demonstrates a new framework for autonomous driving which moves away from reliance on defined logical rules, mapping, and direct supervision. We discuss the challenges and opportunities to scale this approach to a broader range of autonomous driving tasks.

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 "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-3 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning"):
  with members:
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-5 (induced by source paper "badgr an autonomous self supervised learning based navigation system"):
  with members:
    - "deep dynamics models for learning dexterous manipulation"
    - "deep visual mpc policy learning for navigation"
    - "neural autonomous navigation with riemannian motion policy"
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "deep ordinal regression network for monocular depth estimation"
    - "semi parametric topological memory for navigation"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "learning deployable navigation policies at kilometer scale from a single traversal"

Connected through hyperedge HE-6 (induced by source paper "integrating deep reinforcement learning with model based path planners for automated driving"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer"
    - "simulation based reinforcement learning for real world autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "carla an open urban driving simulator"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-8 (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"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

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.LG(Machine Learning)