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: "a multi scale cnn and curriculum learning strategy for mammogram classification"
  Abstract: Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here, we describe a multi-scale convolutional neural network (CNN) trained with a curriculum learning strategy that achieves high levels of accuracy in classifying mammograms. Specifically, we first train CNN-based patch classifiers on segmentation masks of lesions in mammograms, and then use the learned features to initialize a scanning-based model that renders a decision on the whole image, trained end-to-end on outcome data. We demonstrate that our approach effectively handles the "needle in a haystack" nature of full-image mammogram classification, achieving 0.92 AUROC on the DDSM dataset.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "hard pixel mining for depth privileged semantic segmentation"):
  with members:
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "deep learning under privileged information using heteroscedastic dropout"
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-3 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "a study of bfloat16 for deep learning training"
    - "continual lifelong learning with neural networks a review"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "varifocal net a chromosome classification approach using deep convolutional networks"):
  with members:
    - "multi scale convolutional neural networks for crowd counting"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "classification and detection in mammograms with weak supervision via dual branch deep neural net"):
  with members:
    - "optimizing region selection for weakly supervised object detection"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "weakly supervised deep detection networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (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-7 (induced by source paper "mammography assessment using multi scale deep classifiers"):
  with members:
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "human pose estimation via convolutional part heatmap regression"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "a unified mammogram analysis method via hybrid deep supervision"):
  with members:
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)