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: "elements of effective deep reinforcement learning towards tactical driving decision making"
  Abstract: Tactical driving decision making is crucial for autonomous driving systems and has attracted considerable interest in recent years. In this paper, we propose several practical components that can speed up deep reinforcement learning algorithms towards tactical decision making tasks: 1) non-uniform action skipping as a more stable alternative to action-repetition frame skipping, 2) a counter-based penalty for lanes on which ego vehicle has less right-of-road, and 3) heuristic inference-time action masking for apparently undesirable actions. We evaluate the proposed components in a realistic driving simulator and compare them with several baselines. Results show that the proposed scheme provides superior performance in terms of safety, efficiency, and comfort.

Connected through hyperedge HE-1 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "limits of end to end learning"
    - "on a formal model of safe and scalable self driving cars"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "brain inspired cognitive model with attention for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "social attention for autonomous decision making in dense traffic"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "deep imitative models for flexible inference planning and control"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "learning to drive in a day"
    - "deeptraffic crowdsourced hyperparameter tuning of deep reinforcement learning systems for multi agent dense traffic navigation"
    - "recurrent world models facilitate policy evolution"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"):
  with members:
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "encoding motion primitives for autonomous vehicles using virtual velocity constraints and neural network scheduling"):
  with members:
    - "structured control nets for deep reinforcement learning"
    - "automating vehicles by deep reinforcement learning using task separation with hill climbing"
    - "es is more than just a traditional finite difference approximator"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "end to end learning for self driving cars"
    - "deep residual learning for image recognition"

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)