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 "enhancing traffic scene predictions with generative adversarial networks"):
  Abstract: We present a new two-stage pipeline for predicting frames of traffic scenes where relevant objects can still reliably be detected. Using a recent video prediction network, we first generate a sequence of future frames based on past frames. A second network then enhances these frames in order to make them appear more realistic. This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars. To verify this two-stage approach, we conducted experiments on the Cityscapes dataset. For enhancing, we trained two image-to-image translation methods based on generative adversarial networks, one for blind motion deblurring and one for image super-resolution. All resulting predictions were quantitatively evaluated using both traditional metrics and a state-of-the-art object detection network showing that the enhanced frames appear qualitatively improved. While the traditional image comparison metrics, i.e., MSE, PSNR, and SSIM, failed to confirm this visual impression, the object detection evaluation resembles it well. The best performing prediction-enhancement pipeline is able to increase the average precision values for detecting cars by about 9% for each prediction step, compared to the non-enhanced predictions.

Includes members:
  Member "sequential attend infer repeat generative modelling of moving objects":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "learning to forecast and refine residual motion for image to video generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "first order motion model for image animation")
  Member "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction":
    also connected through other hyperedges:
      (none)
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from here to there video inbetweening using direct 3d convolutions")
  Member "disentangling propagation and generation for video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "novel video prediction for large scale scene using optical flow":
    also connected through other hyperedges:
      (none)
  Member "bayesian prediction of future street scenes using synthetic likelihoods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction")

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)