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: "rvos end to end recurrent network for video object segmentation"
  Abstract: Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose a Recurrent network for multiple object Video Object Segmentation (RVOS) that is fully end-to-end trainable. Our model incorporates recurrence on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark. Moreover, our model achieves faster inference runtimes than previous methods, reaching 44ms/frame on a P100 GPU.

Connected through hyperedge HE-1 (induced by source paper "mast a memory augmented self supervised tracker"):
  with members:
    - "video object segmentation using space time memory networks"
    - "self supervised learning for video correspondence flow"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "learning correspondence from the cycle consistency of time"
    - "joint task self supervised learning for temporal correspondence"
    - "unsupervised deep tracking"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "motion attentive transition for zero shot video object segmentation"
    - "self supervised learning for video correspondence flow"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "multi source weak supervision for saliency detection"
    - "learning correspondence from the cycle consistency of time"

Connected through hyperedge HE-4 (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"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "learning human object interactions by graph parsing neural networks"
    - "videos as space time region graphs"

Connected through hyperedge HE-6 (induced by source paper "directional deep embedding and appearance learning for fast video object segmentation"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "atom accurate tracking by overlap maximization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "directional statistics based deep metric learning for image classification and retrieval"

Connected through hyperedge HE-7 (induced by source paper "unovost unsupervised offline video object segmentation and tracking"):
  with members:
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "video instance segmentation"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "towards segmenting everything that moves"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"

Connected through hyperedge HE-8 (induced by source paper "motion attentive transition for zero shot video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
    - "cbam convolutional block attention module"
    - "learning to segment moving objects"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

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)