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: "one shot video object segmentation"
  Abstract: This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

Connected through hyperedge HE-1 (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-2 (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"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a deep one shot network for query based logo retrieval"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "texture synthesis guided deep hashing for texture image retrieval"
    - "multi scale location aware kernel representation for object detection"
    - "open logo detection challenge"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "scalable deep learning logo detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "spectral signatures in backdoor attacks"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-5 (induced by source paper "face mask extraction in video sequence"):
  with members:
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "end to end semantic face segmentation with conditional random fields as convolutional recurrent and adversarial networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clockwork convnets for video semantic segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
    - "visual speech recognition using pca networks and lstms in a tandem gmm hmm system"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "rpm net robust pixel level matching networks for self supervised video object segmentation"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "path aggregation network for instance segmentation"
    - "youtube vos a large scale video object segmentation benchmark"

Connected through hyperedge HE-7 (induced by source paper "visual coin tracking tracking of planar double sided objects"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "unveiling the power of deep tracking"
    - "multi hierarchical independent correlation filters for visual tracking"
    - "youtube vos sequence to sequence video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "learning regression and verification networks for long term visual tracking"
    - "continual occlusions and optical flow estimation"

Connected through hyperedge HE-8 (induced by source paper "squeeze excite guided few shot segmentation of volumetric images"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "few shot segmentation propagation with guided networks"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "one shot learning for semantic segmentation"
    - "learning feed forward one shot learners"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "matching networks for one shot learning"

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)