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: "end to end training for whole image breast cancer diagnosis using an all convolutional design"
  Abstract: We develop an end-to-end training algorithm for whole-image breast cancer diagnosis based on mammograms. It requires lesion annotations only at the first stage of training. After that, a whole image classifier can be trained using only image level labels. This greatly reduced the reliance on lesion annotations. Our approach is implemented using an all convolutional design that is simple yet provides superior performance in comparison with the previous methods. On DDSM, our best single-model achieves a per-image AUC score of 0.88 and three-model averaging increases the score to 0.91. On INbreast, our best single-model achieves a per-image AUC score of 0.96. Using DDSM as benchmark, our models compare favorably with the current state-of-the-art. We also demonstrate that a whole image model trained on DDSM can be easily transferred to INbreast without using its lesion annotations and using only a small amount of training data. Code availability: this https URL

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"
    - "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"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"

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"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "rank pooling for action recognition"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "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 "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"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "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"
    - "semi supervised classification with graph convolutional 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 "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "data efficient image recognition with contrastive predictive coding"
    - "cluster based learning from weakly labeled bags in digital pathology"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "classification of breast cancer histology using deep learning"
    - "smooth loss functions for deep top k classification"

Connected through hyperedge HE-7 (induced by source paper "a dual branch deep neural network for classification and detection in mammograms"):
  with members:
    - "optimizing region selection for weakly supervised object detection"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "weakly supervised deep detection networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-8 (induced by source paper "mammo a deep learning solution for facilitating radiologist machine collaboration in breast cancer diagnosis"):
  with members:
    - "understanding neural networks through deep visualization"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"

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)