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: "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
  Abstract: This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using global image may be affected by the (excessive) irrelevant noisy areas. 2) Due to the poor alignment of some CXR images, the existence of irregular borders hinders the network performance. In this paper, we address the above problems by proposing a three-branch attention guided convolution neural network (AG-CNN). AG-CNN 1) learns from disease-specific regions to avoid noise and improve alignment, 2) also integrates a global branch to compensate the lost discriminative cues by local branch. Specifically, we first learn a global CNN branch using global images. Then, guided by the attention heat map generated from the global branch, we inference a mask to crop a discriminative region from the global image. The local region is used for training a local CNN branch. Lastly, we concatenate the last pooling layers of both the global and local branches for fine-tuning the fusion branch. The Comprehensive experiment is conducted on the ChestX-ray14 dataset. We first report a strong global baseline producing an average AUC of 0.841 with ResNet-50 as backbone. After combining the local cues with the global information, AG-CNN improves the average AUC to 0.868. While DenseNet-121 is used, the average AUC achieves 0.871, which is a new state of the art in the community.

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 "dynamic graph correlation learning for disease diagnosis with incomplete labels"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"
    - "multi label image recognition with graph convolutional networks"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-3 (induced by source paper "localization of critical findings in chest x ray without local annotations using multi instance learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for chest x ray abnormality classification on noisy labels"):
  with members:
    - "chestnet a deep neural network for classification of thoracic diseases on chest radiography"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "scalable and accurate deep learning for electronic health records"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "localization with limited annotation for chest x rays"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "focal loss for dense object detection"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "mask r cnn"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-6 (induced by source paper "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"):
  with members:
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep 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"
    - "data distillation towards omni supervised learning"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"

Connected through hyperedge HE-7 (induced by source paper "quantifying and leveraging classification uncertainty for chest radiograph assessment"):
  with members:
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "evidential deep learning to quantify classification uncertainty"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised medical diagnosis and localization from multiple resolutions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based deep multiple instance learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "classification and disease localization in histopathology using only global labels a weakly supervised approach"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "constrained deep weak supervision for histopathology image segmentation"

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)