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: "semi supervised domain adaptation for weakly labeled semantic video object segmentation"
  Abstract: Deep convolutional neural networks (CNNs) have been immensely successful in many high-level computer vision tasks given large labeled datasets. However, for video semantic object segmentation, a domain where labels are scarce, effectively exploiting the representation power of CNN with limited training data remains a challenge. Simply borrowing the existing pretrained CNN image recognition model for video segmentation task can severely hurt performance. We propose a semi-supervised approach to adapting CNN image recognition model trained from labeled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of video data. By explicitly modeling and compensating for the domain shift from the source domain to the target domain, this proposed approach underpins a robust semantic object segmentation method against the changes in appearance, shape and occlusion in natural videos. We present extensive experiments on challenging datasets that demonstrate the superior performance of our approach compared with the state-of-the-art methods.

Connected through hyperedge HE-1 (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-2 (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"
    - "one shot 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-3 (induced by source paper "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bottom up top down cues for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using motion cues"
    - "video scene parsing with predictive feature learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"

Connected through hyperedge HE-4 (induced by source paper "automatic real time background cut for portrait videos"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "unsupervised video segmentation via spatio temporally nonlocal appearance learning"
    - "recurrent fully convolutional networks for video segmentation"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "learning video object segmentation from static images"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "convolutional oriented boundaries"
    - "fully convolutional siamese networks for object tracking"
    - "click carving segmenting objects in video with point clicks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "one shot video object segmentation"
    - "learning to track at 100 fps with deep regression networks"
    - "deep interactive object selection"

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)