human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"):
  Abstract: The decision and planning system for autonomous driving in urban environments is hard to design. Most current methods manually design the driving policy, which can be expensive to develop and maintain at scale. Instead, with imitation learning we only need to collect data and the computer will learn and improve the driving policy automatically. However, existing imitation learning methods for autonomous driving are hardly performing well for complex urban scenarios. Moreover, the safety is not guaranteed when we use a deep neural network policy. In this paper, we proposed a framework to learn the driving policy in urban scenarios efficiently given offline connected driving data, with a safety controller incorporated to guarantee safety at test time. The experiments show that our method can achieve high performance in realistic simulations of urban driving scenarios.

Includes members:
  Member "multimodal trajectory predictions for autonomous driving using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "conditional affordance learning for driving in urban environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "chauffeurnet learning to drive by imitating the best and synthesizing the worst":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "laserflow efficient and probabilistic object detection and motion forecasting")
  Member "deep object centric policies for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "carla an open urban driving simulator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "explaining how a deep neural network trained with end to end learning steers a car":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "a fast integrated planning and control framework for autonomous driving via imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient motion planning for automated lane change based on imitation learning and mixed integer optimization")

Question: Please tell me which class the source paper 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)