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 "visual attention on the sun what do existing models actually predict"):
  Abstract: Visual attention prediction is a classic problem that seems to be well addressed in the deep learning era. One compelling concern, however, gradually arise along with the rapidly growing performance scores over existing visual attention datasets: do existing deep models really capture the inherent mechanism of human visual attention? To address this concern, this paper proposes a new dataset, named VASUN, that records the free-viewing human attention on solar images. Different from previous datasets, images in VASUN contain many irregular visual patterns that existing deep models have never seen. By benchmarking existing models on VASUN, we find the performances of many state-of-the-art deep models drop remarkably, while many classic shallow models perform impressively. From these results, we find that the significant performance advance of existing deep attention models may come from their capabilities of memorizing and predicting the occurrence of some specific visual patterns other than learning the inherent mechanism of human visual attention. In addition, we also train several baseline models on VASUN to demonstrate the feasibility and key issues of predicting visual attention on the sun. These baseline models, together with the proposed dataset, can be used to revisit the problem of visual attention prediction from a novel perspective that are complementary to existing ones.

Includes members:
  Member "deep visual attention prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "shallow and deep convolutional networks for saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "what do different evaluation metrics tell us about saliency models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "visual saliency based on multiscale deep features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deep gaze i boosting saliency prediction with feature maps trained on imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "the secrets of salient object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "visual saliency based on scale space analysis in the frequency domain":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")

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)