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 "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  Abstract: The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, while the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach is comparable to 11 state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS and 1,490.5 FPS on the GPU and CPU platforms, respectively.

Includes members:
  Member "salient object detection in the deep learning era an in depth survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross layer feature pyramid network for salient object detection")
  Member "adversarial spatio temporal learning for video deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blurry video frame interpolation")
  Member "deep visual attention prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "revisiting video saliency a large scale benchmark and a new model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network")
  Member "video salient object detection via fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "how drones look crowdsourced knowledge transfer for aerial video saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ultrafast video attention prediction with coupled knowledge distillation")
  Member "deep mutual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data")

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)