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 "unsupervised online video object segmentation with motion property understanding"):
  Abstract: Unsupervised online video object segmentation (VOS) aims to automatically segment the moving objects over an unconstrained video without the requirements of any prior information about the objects or camera motion. It is therefore a very challenging problem for high-level video analysis. So far, limited number of such methods have been reported in literature and most of them still have distance to a satisfactory performance. Targeting this challenging problem,in this paper, we propose a novel unsupervised online VOS framework by understanding the motion property as the meaning of \emph{moving} in concurrence with \emph{a generic object} for the segmented regions. By incorporating \emph{salient motion detection} and \emph{object proposal}, a pixel-wise fusion strategy is developed to effectively remove detection noises such as background movements and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is proposed to deal with the unreliable motion detection and object proposals. #R##N#Experimental results on DAVIS-2016 and SegTrack-v2 benchmark dataset show that the proposed method outperforms the other state-of-the-art unsupervised online segmentation by achieving 5.6\% absolute improvement at least, and additionally even achieves a better performance than the best unsupervised offline method on DAVIS-2016 dataset. Another significant advantage also need to be addressed that in all the experiments, there is only one existing trained model for object proposal (Mask RCNN on COCO dataset) being used without any fine-tuning, which is the demonstration of robustness. The most contribution of this work might sheds light on the potential and to motivate more VOS framework studies based on characteristic motion properties.

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "fast and accurate online video object segmentation via tracking parts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "instance embedding transfer to unsupervised video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "efficient video object segmentation via network modulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "robust visual tracking revisited from correlation filter to template matching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time correlation tracking via joint model compression and transfer")
  Member "lucid data dreaming for video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation")
  Member "online adaptation of convolutional neural networks for video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)