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: "mhp vos multiple hypotheses propagation for video object segmentation"
  Abstract: We address the problem of semi-supervised video object segmentation (VOS), where the masks of objects of interests are given in the first frame of an input video. To deal with challenging cases where objects are occluded or missing, previous work relies on greedy data association strategies that make decisions for each frame individually. In this paper, we propose a novel approach to defer the decision making for a target object in each frame, until a global view can be established with the entire video being taken into consideration. Our approach is in the same spirit as Multiple Hypotheses Tracking (MHT) methods, making several critical adaptations for the VOS problem. We employ the bounding box (bbox) hypothesis for tracking tree formation, and the multiple hypotheses are spawned by propagating the preceding bbox into the detected bbox proposals within a gated region starting from the initial object mask in the first frame. The gated region is determined by a gating scheme which takes into account a more comprehensive motion model rather than the simple Kalman filtering model in traditional MHT. To further design more customized algorithms tailored for VOS, we develop a novel mask propagation score instead of the appearance similarity score that could be brittle due to large deformations. The mask propagation score, together with the motion score, determines the affinity between the hypotheses during tree pruning. Finally, a novel mask merging strategy is employed to handle mask conflicts between objects. Extensive experiments on challenging datasets demonstrate the effectiveness of the proposed method, especially in the case of object missing.

Connected through hyperedge HE-1 (induced by source paper "dual temporal memory network for efficient video object segmentation"):
  with members:
    - "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"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "an empirical study of propagation based methods for video object segmentation"):
  with members:
    - "discriminative online learning for fast video object segmentation"
    - "video object segmentation using space time memory networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "a generative appearance model for end to end video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "temporally coherent video harmonization using adversarial networks"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "imagine this scripts to compositions to videos"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "dual motion gan for future flow embedded video prediction"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"

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)