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 salient object detection using image labels"
  Abstract: Deep learning based salient object detection has recently achieved great success with its performance greatly outperforms any other unsupervised methods. However, annotating per-pixel saliency masks is a tedious and inefficient procedure. In this paper, we note that superior salient object detection can be obtained by iteratively mining and correcting the labeling ambiguity on saliency maps from traditional unsupervised methods. We propose to use the combination of a coarse salient object activation map from the classification network and saliency maps generated from unsupervised methods as pixel-level annotation, and develop a simple yet very effective algorithm to train fully convolutional networks for salient object detection supervised by these noisy annotations. Our algorithm is based on alternately exploiting a graphical model and training a fully convolutional network for model updating. The graphical model corrects the internal labeling ambiguity through spatial consistency and structure preserving while the fully convolutional network helps to correct the cross-image semantic ambiguity and simultaneously update the coarse activation map for next iteration. Experimental results demonstrate that our proposed method greatly outperforms all state-of-the-art unsupervised saliency detection methods and can be comparable to the current best strongly-supervised methods training with thousands of pixel-level saliency map annotations on all public benchmarks.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised salient object detection via scribble annotations"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "gated crf loss for weakly supervised semantic image segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "enhanced alignment measure for binary foreground map evaluation"

Connected through hyperedge HE-2 (induced by source paper "rosa robust salient object detection against adversarial attacks"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "spatially transformed adversarial examples"
    - "adversarial attack on graph structured data"
    - "contrast oriented deep neural networks for salient object detection"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "reverse attention for salient object detection"
    - "hierarchical cellular automata for visual saliency"
    - "learning uncertain convolutional features for accurate saliency detection"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"

Connected through hyperedge HE-4 (induced by source paper "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "salient object detection by lossless feature reflection"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "a fast and compact saliency score regression network based on fully convolutional network"

Connected through hyperedge HE-5 (induced by source paper "semi supervised video salient object detection using pseudo labels"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "video salient object detection via fully convolutional networks"
    - "non local neural networks"
    - "structure measure a new way to evaluate foreground maps"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "learning video object segmentation with visual memory"
    - "segflow joint learning for video object segmentation and optical flow"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "contrast oriented deep neural networks for salient object detection"):
  with members:
    - "multi label image recognition by recurrently discovering attentional regions"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "instance level salient object segmentation"
    - "deep contrast learning for salient object detection"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "visual saliency detection based on multiscale deep cnn features"
    - "holistically nested edge detection"

Connected through hyperedge HE-7 (induced by source paper "an attention based approach for single image super resolution"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "deep edge guided recurrent residual learning for image super resolution"
    - "attention is all you need"
    - "enhanced deep residual networks for single image super resolution"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

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)