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: "detecting and classifying lesions in mammograms with deep learning"
  Abstract: In the last two decades Computer Aided Diagnostics (CAD) systems were developed to help radiologists analyze screening mammograms. The benefits of current CAD technologies appear to be contradictory and they should be improved to be ultimately considered useful. Since 2012 deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. Our approach described here has achieved the 2nd place in the Digital Mammography DREAM Challenge with $ AUC = 0.85 $. The proposed method also sets the state of the art classification performance on the public INbreast database, $ AUC = 0.95$. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset.

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 "weakly and semi supervised detection in medical imaging via deep dual branch net"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "optimizing region selection for weakly supervised object detection"
    - "who said what modeling individual labelers improves classification"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"
    - "weakly and semi supervised object detection with expectation maximization algorithm"

Connected through hyperedge HE-3 (induced by source paper "joint 2d 3d breast cancer classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rank pooling for action recognition"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a hypersensitive breast cancer detector"):
  with members:
    - "automated soft tissue lesion detection and segmentation in digital mammography using a u net deep learning network"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "u net convolutional networks for biomedical image segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "adversarial augmentation for enhancing classification of mammography images"):
  with members:
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "injecting and removing malignant features in mammography with cyclegan investigation of an automated adversarial attack using neural networks"
    - "multimodal unsupervised image to image translation"
    - "pathology segmentation using distributional differences to images of healthy origin"
    - "an adversarial learning approach to medical image synthesis for lesion removal"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "progressive growing of gans for improved quality stability and variation"
    - "checkerboard artifact free sub pixel convolution a note on sub pixel convolution resize convolution and convolution resize"

Connected through hyperedge HE-6 (induced by source paper "computer aided detection of squamous carcinoma of the cervix in whole slide images"):
  with members:
    - "deep learning framework for multi class breast cancer histology image classification"
    - "cancer metastasis detection with neural conditional random field"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "recent advances in the applications of convolutional neural networks to medical image contour detection"
    - "deep retinal image understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identifying metastases in sentinel lymph nodes with deep convolutional neural networks"
    - "patch based convolutional neural network for whole slide tissue image classification"

Connected through hyperedge HE-7 (induced by source paper "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "mask r cnn"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "globally aware multiple instance classifier for breast cancer screening"):
  with members:
    - "attention based deep multiple instance learning"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "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"
    - "weakly supervised cascaded convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

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)