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: "breast mass classification from mammograms using deep convolutional neural networks"
  Abstract: Mammography is the most widely used method to screen breast cancer. Because of its mostly manual nature, variability in mass appearance, and low signal-to-noise ratio, a significant number of breast masses are missed or misdiagnosed. In this work, we present how Convolutional Neural Networks can be used to directly classify pre-segmented breast masses in mammograms as benign or malignant, using a combination of transfer learning, careful pre-processing and data augmentation to overcome limited training data. We achieve state-of-the-art results on the DDSM dataset, surpassing human performance, and show interpretability of our model.

Connected through hyperedge HE-1 (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-2 (induced by source paper "conditional infilling gans for data augmentation in mammogram classification"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "on convergence and stability of gans"
    - "low shot learning from imaginary data"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "unsupervised histopathology image synthesis"
    - "improved training of wasserstein gans"
    - "generalization of deep neural networks for chest pathology classification in x rays using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "sd cnn a shallow deep cnn for improved breast cancer diagnosis"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "deep learning to improve breast cancer early detection on screening mammography"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "r fcn object detection via region based fully convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "a multi scale cnn and curriculum learning strategy for mammogram classification"):
  with members:
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "optimizing and visualizing deep learning for benign malignant classification in breast tumors"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "adversarial deep structural networks for mammographic mass segmentation"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "end to end training for whole image breast cancer diagnosis using an all convolutional design"):
  with members:
    - "r fcn object detection via region based fully convolutional networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "high resolution breast cancer screening with multi view deep convolutional neural networks"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "understanding neural networks through deep visualization"
    - "multi view convolutional neural networks for 3d shape recognition"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "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)