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 "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"):
  Abstract: In ground-level platforms, many saliency models have been developed to perceive the visual world as the human does. However, they may not fit a drone that can look from many abnormal viewpoints. To address this problem, this paper proposes a Crowdsourced Multi-path Network (CMNet) that transfer the ground-level knowledge for spatiotemporal saliency prediction in aerial videos. To train CMNet, we first collect and fuse the eye-tracking data of 24 subjects on 1,000 aerial videos to annotate the ground-truth salient regions. Inspired by the crowdsourced annotation in eye-tracking experiments, we design a multi-path architecture for CMNet, in which each path is initialized under the supervision of a classic ground-level saliency model. After that, the most representative paths are selected in a data-driven manner, which are then fused and simultaneously fine-tuned on aerial videos. In this manner, the prior knowledge in various classic ground-level saliency models can be transferred into CMNet so as to improve its capability in processing aerial videos. Finally, the spatial predictions given by CMNet are adaptively refined by incorporating the temporal saliency predictions via a spatiotemporal saliency optimization algorithm. Experimental results show that the proposed approach outperforms ten state-of-the-art models in predicting visual saliency in aerial videos.

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 "revisiting video saliency a large scale benchmark and a new model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "end to end saliency mapping via probability distribution prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "video salient object detection via fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "squeeze and excitation networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "saliency detection by forward and backward cues in deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "wepsam weakly pre learnt saliency model":
    also connected through other hyperedges:
      (none)
  Member "spatio temporal saliency networks for dynamic saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)