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 "efficient latent representations using multiple tasks for autonomous driving"):
  Abstract: Driving in the dynamic, multi-agent, and complex urban environment is a difficult task requiring a complex decision policy. The learning of such a policy requires a state representation that can encode the entire environment. Mid-level representations that encode a vehicle's environment as images have become a popular choice, but they are quite high-dimensional, which limits their use in data-scarce cases such as reinforcement learning. In this article, we propose to learn a low dimensional and rich feature representation of the environment by training an encoder-decoder deep neural network to predict multiple application relevant factors such as trajectories of other agents. We demonstrate that the use of the multi-head encoder-decoder neural network results in a more informative representation compared to a single-head encoder-decoder model. In particular, the proposed representation learning approach helps the policy network to learn faster, with increased performance and with less data, compared to existing approaches using a single-head network.

Includes members:
  Member "urban driving with conditional imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning")
  Member "deep kinematic models for physically realistic prediction of vehicle trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non gaussian chance constrained trajectory planning for autonomous vehicles in the presence of uncertain agents")
  Member "predicting motion of vulnerable road users using high definition maps and efficient convnets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "model free deep reinforcement learning for urban autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "covernet multimodal behavior prediction using trajectory sets":
    also connected through other hyperedges:
      (none)
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)