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 "sg fcn a motion and memory based deep learning model for video saliency detection"):
  Abstract: Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of image-based salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

Includes members:
  Member "predicting human eye fixations via an lstm based saliency attentive model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how much position information do convolutional neural networks encode")
  Member "exploring models and data for remote sensing image caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures")
  Member "learning uncertain convolutional features for accurate saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "geometric hypergraph learning for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "handcrafted and deep trackers recent visual object tracking approaches and trends")
  Member "deepsaliency multi task deep neural network model for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "adaptive compressive tracking via online vector boosting feature selection":
    also connected through other hyperedges:
      (none)
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)