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 "learning to predict ego vehicle poses for sampling based nonholonomic motion planning"):
  Abstract: Sampling-based motion planning is an effective tool to compute safe trajectories for automated vehicles in complex environments. However, a fast convergence to the optimal solution can only be ensured with the use of problem-specific sampling distributions. Due to the large variety of driving situations within the context of automated driving, it is very challenging to manually design such distributions. This paper introduces therefore a data-driven approach utilizing a deep convolutional neural network (CNN): Given the current driving situation, future ego-vehicle poses can be directly generated from the output of the CNN allowing to guide the motion planner efficiently towards the optimal solution. A benchmark highlights that the CNN predicts future vehicle poses with a higher accuracy compared to uniform sampling and a state-of-the-art A*-based approach. Combining this CNN-guided sampling with the motion planner Bidirectional RRT* reduces the computation time by up to an order of magnitude and yields a faster convergence to a lower cost as well as a success rate of 100 % in the tested scenarios.

Includes members:
  Member "learning human aware path planning with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision")
  Member "universal planning networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "motion planning networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "learning sampling distributions for robot motion planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerating cooperative planning for automated vehicles with learned heuristics and monte carlo tree search")
  Member "lidar based driving path generation using fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey of deep learning applications to autonomous vehicle control")
  Member "data driven planning via imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bayesian residual policy optimization scalable bayesian reinforcement learning with clairvoyant experts")
  Member "learning from the hindsight plan episodic mpc improvement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "information theoretic model predictive q learning")
  Member "end to end learning for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)