human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
  Abstract: Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and their robust as well as accurate diffusion across time. We evaluate our proposed algorithm on the challenging DAVIS, SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge detector trained on 200 images, our method achieves state-of-the-art results outperforming deep learning based methods in the unsupervised setting. We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.

Connected through hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "dual temporal memory network for efficient video object segmentation"):
  with members:
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "video object segmentation using space time memory networks"
    - "capsulevos semi supervised video object segmentation using capsule routing"
    - "spatiotemporal cnn for video object segmentation"
    - "lip learning instance propagation for video object segmentation"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "zero shot video object segmentation via attentive graph neural networks"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "reasoning visual dialogs with structural and partial observations"
    - "rvos end to end recurrent network for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-4 (induced by source paper "video saliency prediction using enhanced spatiotemporal alignment network"):
  with members:
    - "reppoints point set representation for object detection"
    - "simple vs complex temporal recurrences for video saliency prediction"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "deepvs a deep learning based video saliency prediction approach"
    - "tdan temporally deformable alignment network for video super resolution"
    - "deformable convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "revisiting saliency metrics farthest neighbor area under curve"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "saliency benchmarking made easy separating models maps and metrics"
    - "residual attention network for image classification"
    - "a deep multi level network for saliency prediction"
    - "what do different evaluation metrics tell us about saliency models"
    - "cat2000 a large scale fixation dataset for boosting saliency research"
    - "deep gaze i boosting saliency prediction with feature maps trained on imagenet"

Connected through hyperedge HE-6 (induced by source paper "video object segmentation and tracking a survey"):
  with members:
    - "interactive video object segmentation in the wild"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "youtube vos sequence to sequence video object segmentation"

Connected through hyperedge HE-7 (induced by source paper "automatic video object segmentation via motion appearance stream fusion and instance aware segmentation"):
  with members:
    - "a fully convolutional two stream fusion network for interactive image segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "maskrnn instance level video object segmentation"
    - "video object segmentation with re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the 2017 davis challenge on video object segmentation"

Connected through hyperedge HE-8 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "cornernet detecting objects as paired keypoints"
    - "deep cosine metric learning for person re identification"
    - "event based moving object detection and tracking"

Question: Please tell me which class the center node 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)