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: "model free deep reinforcement learning for urban autonomous driving"
  Abstract: Urban autonomous driving decision making is challenging due to complex road geometry and multi-agent interactions. Current decision making methods are mostly manually designing the driving policy, which might result in sub-optimal solutions and is expensive to develop, generalize and maintain at scale. On the other hand, with reinforcement learning (RL), a policy can be learned and improved automatically without any manual designs. However, current RL methods generally do not work well on complex urban scenarios. In this paper, we propose a framework to enable model-free deep reinforcement learning in challenging urban autonomous driving scenarios. We design a specific input representation and use visual encoding to capture the low-dimensional latent states. Several state-of-the-art model-free deep RL algorithms are implemented into our framework, with several tricks to improve their performance. We evaluate our method in a challenging roundabout task with dense surrounding vehicles in a high-definition driving simulator. The result shows that our method can solve the task well and is significantly better than the baseline.

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"
    - "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"
    - "a tempt to unify heterogeneous driving databases using traffic primitives"

Connected through hyperedge HE-2 (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"
    - "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"
    - "world models"

Connected through hyperedge HE-3 (induced by source paper "efficient latent representations using multiple tasks for autonomous driving"):
  with members:
    - "urban driving with conditional imitation learning"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "covernet multimodal behavior prediction using trajectory sets"
    - "pytorch an imperative style high performance deep learning library"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "towards learning multi agent negotiations via self play"):
  with members:
    - "carla an open urban driving simulator"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "on a formal model of safe and scalable self driving cars"
    - "end to end driving via conditional imitation learning"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-5 (induced by source paper "task motion planning for safe and efficient urban driving"):
  with members:
    - "interpretable end to end urban autonomous driving with latent deep reinforcement learning"
    - "learning from interventions using hierarchical policies for safe learning"
    - "augmenting knowledge through statistical goal oriented human robot dialog"
    - "learning to guide task and motion planning using score space representation"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "carla an open urban driving simulator"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-6 (induced by source paper "simulation based reinforcement learning for real world autonomous driving"):
  with members:
    - "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"
    - "conditional affordance learning for driving in urban environments"

Connected through hyperedge HE-7 (induced by source paper "driving in dense traffic with model free reinforcement learning"):
  with members:
    - "cooperation aware lane change control in dense traffic"
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"
    - "learning agile and dynamic motor skills for legged robots"
    - "search based optimal motion planning for automated driving"
    - "an introduction to deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "fully bayesian recurrent neural networks for safe reinforcement learning"):
  with members:
    - "safe reinforcement learning with model uncertainty estimates"
    - "social attention modeling attention in human crowds"
    - "bayesian recurrent neural networks"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "concrete problems in ai safety"
    - "safe multi agent reinforcement learning for autonomous driving"

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)