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 "interaction aware kalman neural networks for trajectory prediction"):
  Abstract: Forecasting the motion of surrounding dynamic obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for autonomous vehicles. Complex traffic scenes yield great challenges in modeling the traffic patterns of surrounding dynamic obstacles. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction-aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for trajectory prediction.

Includes members:
  Member "convolutional social pooling for vehicle trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sequential forecasting of 100 000 points")
  Member "trafficpredict trajectory prediction for heterogeneous traffic agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms")
  Member "social gan socially acceptable trajectories with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  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 "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "baidu apollo em motion planner":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overview of tools supporting planning for automated driving")
  Member "multi agent imitation learning for driving simulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "imitating driver behavior with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial imitation learning with neural networks global optimality and convergence rate")

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)