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 "video saliency prediction using enhanced spatiotemporal alignment network"):
  Abstract: Due to a variety of motions across different frames, it is highly challenging to learn an effective spatiotemporal representation for accurate video saliency prediction (VSP). To address this issue, we develop an effective spatiotemporal feature alignment network tailored to VSP, mainly including two key sub-networks: a multi-scale deformable convolutional alignment network (MDAN) and a bidirectional convolutional Long Short-Term Memory (Bi-ConvLSTM) network. The MDAN learns to align the features of the neighboring frames to the reference one in a coarse-to-fine manner, which can well handle various motions. Specifically, the MDAN owns a pyramidal feature hierarchy structure that first leverages deformable convolution (Dconv) to align the lower-resolution features across frames, and then aggregates the aligned features to align the higher-resolution features, progressively enhancing the features from top to bottom. The output of MDAN is then fed into the Bi-ConvLSTM for further enhancement, which captures the useful long-time temporal information along forward and backward timing directions to effectively guide attention orientation shift prediction under complex scene transformation. Finally, the enhanced features are decoded to generate the predicted saliency map. The proposed model is trained end-to-end without any intricate post processing. Extensive evaluations on four VSP benchmark datasets demonstrate that the proposed method achieves favorable performance against state-of-the-art methods. The source codes and all the results will be released.

Includes members:
  Member "reppoints point set representation for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "simple vs complex temporal recurrences for video saliency prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mediaeval 2019 concealed fgsm perturbations for privacy preservation")
  Member "edvr video restoration with enhanced deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blurry video frame interpolation")
  Member "deep visual attention prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "deepvs a deep learning based video saliency prediction approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "unsupervised video object segmentation using motion saliency guided spatio temporal propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "tdan temporally deformable alignment network for video super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep blind video super resolution")

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)