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 "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network"):
  Abstract: Predicting the future trajectory of surrounding vehicles in a randomly varying traffic level is one of the most challenging problems in developing an autonomous vehicle. Since there is no pre-defined number of interacting vehicles participate in, the prediction network has to be scalable with respect to the vehicle number in order to guarantee the consistency in terms of both accuracy and computational load. In this paper, the first fully scalable trajectory prediction network, SCALE-Net, is proposed that can ensure both higher prediction performance and consistent computational load regardless of the number of surrounding vehicles. The SCALE-Net employs the Edge-enhance Graph Convolutional Neural Network (EGCN) for the inter-vehicular interaction embedding network. Since the proposed EGCN is inherently scalable with respect to the graph node (an agent in this study), the model can be operated independently from the total number of vehicles considered. We evaluated the scalability of the SCALE-Net on the publically available NGSIM datasets by comparing variations on computation time and prediction accuracy per single driving scene with respect to the varying vehicle number. The experimental test shows that both computation time and prediction performance of the SCALE-Net consistently outperform those of previous models regardless of the level of traffic complexities.

Includes members:
  Member "graph neural networks for modelling traffic participant interaction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction")
  Member "interaction aware kalman neural networks for trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving")
  Member "multi agent tensor fusion for contextual trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "rules of the road predicting driving behavior with a convolutional model of semantic interactions":
    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 "convolutional social pooling for vehicle trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequential forecasting of 100 000 points")
  Member "exploiting edge features in graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explain graph neural networks to understand weighted graph features in node classification")
  Member "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "plop probabilistic polynomial objects trajectory planning 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.CV(Computer Vision and Pattern Recognition)