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 learning for saliency prediction in natural video"):
  Abstract: The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. #R##N#Salient patches in video frames are predicted first.#R##N#Then the predicted visual fixation maps are built upon them.  We design the deep architecture on the basis of CaffeNet implemented #R##N#with Caffe toolkit. We show that changing#R##N#the way of data selection for optimisation of network parameters, we can save computation cost up to $12$ times. #R##N#We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity #R##N#of the human visual system to residual motion. #R##N#Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models.  #R##N#The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing $31$ videos with an #R##N#overall amount of $7300$ frames and eye fixations #R##N#of $37$ subjects.#R##N#The second one is HOLLYWOOD2 provided $2517$ movie clips with the eye fixations of $19$ subjects.#R##N#On IRCYYN dataset, the accuracy obtained is of $89.51\% $.#R##N#On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the  improvement up to $2\%$ with regard to RGB use only. #R##N#The resulting accuracy of $76,6\%$ is obtained. #R##N#The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to $16\%$ on a sample of video #R##N#clips from this dataset.

Includes members:
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "two stream convolutional networks for action recognition in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "actions in the eye dynamic gaze datasets and learnt saliency models for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)