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 "uncertainty aware driver trajectory prediction at urban intersections"):
  Abstract: Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors. Our predictor generates both a conditional variational distribution of future trajectories, as well as a confidence estimate for different time horizons. Our approach allows us to handle inherently uncertain situations, and reason about information gain from each input, as well as combine our model with additional predictors, creating a mixture of experts. We show how to augment the variational predictor with a physics-based predictor, and based on their confidence estimations, improve overall system performance. The resulting combined model is aware of the uncertainty associated with its predictions, which can help the vehicle autonomy to make decisions with more confidence. The model is validated on real-world urban driving data collected in multiple locations. This validation demonstrates that our approach improves the prediction error of a physics-based model by 25% while successfully identifying the uncertain cases with 82% accuracy.

Includes members:
  Member "how would surround vehicles move a unified framework for maneuver classification and motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "failing to learn autonomously identifying perception failures for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shadow transfer single image relighting for urban road scenes")
  Member "variational end to end navigation and localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "autorally an open platform for aggressive autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning")
  Member "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "desire distant future prediction in dynamic scenes with interacting agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving")

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)