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: "weakly supervised semantic segmentation based on web image co segmentation"
  Abstract: Training a Fully Convolutional Network (FCN) for semantic segmentation requires a large number of masks with pixel level labelling, which involves a large amount of human labour and time for annotation. In contrast, web images and their image-level labels are much easier and cheaper to obtain. In this work, we propose a novel method for weakly supervised semantic segmentation with only image-level labels. The method utilizes the internet to retrieve a large number of images and uses a large scale co-segmentation framework to generate masks for the retrieved images. We first retrieve images from search engines, e.g. Flickr and Google, using semantic class names as queries, e.g. class names in the dataset PASCAL VOC 2012. We then use high quality masks produced by co-segmentation on the retrieved images as well as the target dataset images with image level labels to train segmentation networks. We obtain an IoU score of 56.9 on test set of PASCAL VOC 2012, which reaches the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "fully using classifiers for weakly supervised semantic segmentation with modified cues"):
  with members:
    - "incorporating network built in priors in weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "saliency guided deep network for weakly supervised image segmentation"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional one shot object segmentation for industrial robotics"):
  with members:
    - "fast convergence for object detection by learning how to combine error functions"
    - "one shot segmentation in clutter"
    - "repmet representative based metric learning for classification and one shot object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "robotic tactile perception of object properties a review"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "deep metric learning with angular loss"
    - "interactive perception leveraging action in perception and perception in action"

Connected through hyperedge HE-3 (induced by source paper "cian cross image affinity net for weakly supervised semantic segmentation"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "semantic aware attention based deep object co segmentation"
    - "ocnet object context network for scene parsing"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "convolutional simplex projection network cspn for weakly supervised semantic segmentation"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "coarse to fine semantic segmentation from image level labels"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "decoupled spatial neural attention for weakly supervised semantic segmentation"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

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)