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: "a deep reinforcement learning based driving policy for autonomous road vehicles"
  Abstract: In this work, the problem of path planning for an autonomous vehicle that moves on a freeway is considered. The most common approaches that are used to address this problem are based on optimal control methods, which make assumptions about the model of the environment and the system dynamics. On the contrary, this work proposes the development of a driving policy based on reinforcement learning. In this way, the proposed driving policy makes minimal or no assumptions about the environment, since a priori knowledge about the system dynamics is not required. Driving scenarios where the road is occupied both by autonomous and manual driving vehicles are considered. To the best of the authors’ knowledge, this is one of the first approaches that propose a reinforcement learning driving policy for mixed driving environments. The derived reinforcement learning policy, firstly, is compared against an optimal policy derived via dynamic programming, and, secondly, its efficiency is evaluated under realistic scenarios generated by the established SUMO microscopic traffic flow simulator. Finally, some initial results regarding the effect of autonomous vehicles’ behaviour on the overall traffic flow are presented.

Connected through hyperedge HE-1 (induced by source paper "integrating deep reinforcement learning with model based path planners for automated driving"):
  with members:
    - "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"
    - "learning to drive in a day"
    - "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-2 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "predicting driver attention in critical situations"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "learning to drive using inverse reinforcement learning and deep q 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.RO(Robotics)