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: "learning video object segmentation from static images"
  Abstract: Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce video object segmentation problem as a concept of guided instance segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using a convnet trained with static images only. The key ingredient of our approach is a combination of offline and online learning strategies, where the former serves to produce a refined mask from the previous frame estimate and the latter allows to capture the appearance of the specific object instance. Our method can handle different types of input annotations: bounding boxes and segments, as well as incorporate multiple annotated frames, making the system suitable for diverse applications. We obtain competitive results on three different datasets, independently from the type of input annotation.

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"
    - "rvos end to end recurrent network for video object segmentation"
    - "feelvos fast end to end embedding learning for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "low latency video semantic segmentation"):
  with members:
    - "semantic video cnns through representation warping"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "video scene parsing with predictive feature learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "joint person segmentation and identification in synchronized first and third person videos"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "identifying first person camera wearers in third person videos"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-4 (induced by source paper "video salient object detection via fully convolutional networks"):
  with members:
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "deep contrast learning for salient object detection"
    - "saliency detection via combining region level and pixel level predictions with cnns"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "personalizing human video pose estimation"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-5 (induced by source paper "actor action semantic segmentation with region masks"):
  with members:
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "std2p rgbd semantic segmentation using spatio temporal data driven pooling"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "region based semantic segmentation with end to end training"
    - "learning to segment object candidates"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-6 (induced by source paper "lucid data dreaming for video object segmentation"):
  with members:
    - "deep spatio temporal random fields for efficient video segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "super trajectory for video segmentation"
    - "guided optical flow learning"
    - "learning video object segmentation with visual memory"
    - "learning from synthetic humans"

Connected through hyperedge HE-7 (induced by source paper "video object segmentation using supervoxel based gerrymandering"):
  with members:
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "video propagation networks"
    - "libsvx a supervoxel library and benchmark for early video processing"

Connected through hyperedge HE-8 (induced by source paper "video propagation networks"):
  with members:
    - "fast optical flow using dense inverse search"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "playing for data ground truth from computer games"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "semantic video segmentation exploring inference efficiency"
    - "multi scale context aggregation by dilated convolutions"

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)