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 kinematic models for physically realistic prediction of vehicle trajectories"):
  Abstract: Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potential one of the critical aspects of the autonomous technology is understanding and predicting future movement of vehicles surrounding the SDV. This work presents a deep-learning-based method for kinematically feasible motion prediction of such traffic actors. Previous work did not explicitly encode vehicle kinematics and instead relied on the models to learn the constraints directly from the data, potentially resulting in kinematically infeasible, suboptimal trajectory predictions. To address this issue we propose a method that seamlessly combines ideas from the AI with physically grounded vehicle motion models. In this way we employ best of the both worlds, coupling powerful learning models with strong feasibility guarantees for their outputs. The proposed approach is general, being applicable to any type of learning method. Extensive experiments using deep convnets on real-world data strongly indicate its benefits, outperforming the existing state-of-the-art.

Includes members:
  Member "sr lstm state refinement for lstm towards pedestrian trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "multi agent tensor fusion for contextual trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "predicting motion of vulnerable road users using high definition maps and efficient convnets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "peeking into the future predicting future person activities and locations in videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "multimodal trajectory predictions for autonomous driving using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential forecasting of 100 000 points")
  Member "uncertainty aware short term motion prediction of traffic actors for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles")

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)