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 medical diagnosis and localization from multiple resolutions"
  Abstract: Diagnostic imaging often requires the simultaneous identification of a multitude of findings of varied size and appearance. Beyond global indication of said findings, the prediction and display of localization information improves trust in and understanding of results when augmenting clinical workflow. Medical training data rarely includes more than global image-level labels as segmentations are time-consuming and expensive to collect. We introduce an approach to managing these practical constraints by applying a novel architecture which learns at multiple resolutions while generating saliency maps with weak supervision. Further, we parameterize the Log-Sum-Exp pooling function with a learnable lower-bounded adaptation (LSE-LBA) to build in a sharpness prior and better handle localizing abnormalities of different sizes using only image-level labels. Applying this approach to interpreting chest x-rays, we set the state of the art on 9 abnormalities in the NIH's CXR14 dataset while generating saliency maps with the highest resolution to date.

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 "when labelled data hurts deep semi supervised classification with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "smooth neighbors on teacher graphs for semi supervised learning"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification"):
  with members:
    - "two cycle learning clustering based regularisation for deep semi supervised classification"
    - "graphx net chest x ray classification under extreme minimal supervision"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "semi supervised classification with graph convolutional networks"

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"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "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 "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"):
  with members:
    - "semi supervised learning by label gradient alignment"
    - "label propagation for deep semi supervised learning"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "deep residual learning for image recognition"

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"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "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 "globally aware multiple instance classifier for breast cancer screening"):
  with members:
    - "attention based deep multiple instance learning"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "breast density classification with deep convolutional neural networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "weakly supervised cascaded convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised universal fracture detection in pelvic x rays"):
  with members:
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "weakly supervised localization and classification of proximal femur fractures"
    - "deep nets what have they ever done for vision"
    - "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"
    - "detecting hip fractures with radiologist level performance using deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

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)