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 from weak and noisy labels for semantic segmentation"
  Abstract: A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these ‘free’ tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel $L_1$ -optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the $L_1$ -optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy.

Connected through hyperedge HE-1 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-2 (induced by source paper "limitations of weak labels for embedding and tagging"):
  with members:
    - "learning problem agnostic speech representations from multiple self supervised tasks"
    - "massively parallel hyperparameter tuning"
    - "a closer look at weak label learning for audio events"
    - "training neural audio classifiers with few data"
    - "adaptive pooling operators for weakly labeled sound event detection"
    - "unsupervised feature learning based on deep models for environmental audio tagging"
    - "prototypical networks for few shot learning"
    - "learning from between class examples for deep sound recognition"

Connected through hyperedge HE-3 (induced by source paper "segmentation aware image denoising without knowing true segmentation"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "connecting image denoising and high level vision tasks via deep learning"

Connected through hyperedge HE-4 (induced by source paper "guided anisotropic diffusion and iterative learning for weakly supervised change detection"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "learning to measure change fully convolutional siamese metric networks for scene change detection"
    - "deep learning is robust to massive label noise"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "fully convolutional siamese networks for change detection"
    - "high resolution semantic change detection"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "deep residual learning for image recognition"
    - "discovering attribute shades of meaning with the crowd"

Connected through hyperedge HE-6 (induced by source paper "cereals cost effective region based active learning for semantic segmentation"):
  with members:
    - "adversarial learning for semi supervised semantic segmentation"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "exploiting saliency for object segmentation from image level labels"

Connected through hyperedge HE-7 (induced by source paper "dasnet reducing pixel level annotations for instance and semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "discovering class specific pixels for weakly supervised semantic segmentation"
    - "ssd single shot multibox detector"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "convolutional neural network for fourier ptychography video reconstruction learning temporal dynamics from spatial ensembles"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "photo realistic single image super resolution using a generative adversarial 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)