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: "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
  Abstract: Automated detection and segmentation of pulmonary nodules on lung computed tomography (CT) scans can facilitate early lung cancer diagnosis. Existing supervised approaches for automated nodule segmentation on CT scans require voxel-based annotations for training, which are labor- and time-consuming to obtain. In this work, we propose a weakly-supervised method that generates accurate voxel-level nodule segmentation trained with image-level labels only. By adapting a convolutional neural network (CNN) trained for image classification, our proposed method learns discriminative regions from the activation maps of convolution units at different scales, and identifies the true nodule location with a novel candidate-screening framework. Experimental results on the public LIDC-IDRI dataset demonstrate that, our weakly-supervised nodule segmentation framework achieves competitive performance compared to a fully-supervised CNN-based segmentation method.

Connected through hyperedge HE-1 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization"):
  with members:
    - "deep neural networks improve radiologists performance in breast cancer screening"
    - "processing megapixel images with deep attention sampling models"
    - "pseudoedgenet nuclei segmentation only with point annotations"
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning from suspected target bootstrapping performance for breast cancer detection in mammography"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "mammo a deep learning solution for facilitating radiologist machine collaboration in breast cancer diagnosis"

Connected through hyperedge HE-2 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

Connected through hyperedge HE-3 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"

Connected through hyperedge HE-5 (induced by source paper "attention based glaucoma detection a large scale database and cnn model"):
  with members:
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "bubbleview an interface for crowdsourcing image importance maps and tracking visual attention"
    - "supervising neural attention models for video captioning by human gaze data"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "evidence localization for pathology images using weakly supervised learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving high resolution histology image classification with deep spatial fusion network"
    - "cbam convolutional block attention module"
    - "rotation equivariant cnns for digital pathology"
    - "detecting cancer metastases on gigapixel pathology images"
    - "deep residual learning for image recognition"
    - "learning deep features for discriminative localization"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-7 (induced by source paper "estimating brain age based on a healthy population with deep learning and structural mri"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gaussian process uncertainty in age estimation as a measure of brain abnormality"
    - "brain age prediction based on resting state functional connectivity patterns using convolutional neural networks"
    - "what is the effect of importance weighting in deep learning"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "pre and post hoc diagnosis and interpretation of malignancy from breast dce mri"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "training medical image analysis systems like radiologists"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "teacher student curriculum learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "gp unet lesion detection from weak labels with a 3d regression 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)