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: "adversarial deep structural networks for mammographic mass segmentation"
  Abstract: Mass segmentation is an important task in mammogram analysis, providing effective morphological features and regions of interest (ROI) for mass detection and classification. Inspired by the success of using deep convolutional features for natural image analysis and conditional random fields (CRF) for structural learning, we propose an end-to-end network for mammographic mass segmentation. The network employs a fully convolutional network (FCN) to model potential function, followed by a CRF to perform structural learning. Because the mass distribution varies greatly with pixel position, the FCN is combined with position priori for the task. Due to the small size of mammogram datasets, we use adversarial training to control over-fitting. Four models with different convolutional kernels are further fused to improve the segmentation results. Experimental results on two public datasets, INbreast and DDSM-BCRP, show that our end-to-end network combined with adversarial training achieves the-state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"

Connected through hyperedge HE-3 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (induced by source paper "boosting segmentation with weak supervision from image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-5 (induced by source paper "towards semi supervised segmentation via image to image translation"):
  with members:
    - "on the convergence of adam and beyond"
    - "multimodal unsupervised image to image translation"
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "spectral normalization for generative adversarial networks"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "pathology segmentation using distributional differences to images of healthy origin"

Connected through hyperedge HE-6 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"):
  with members:
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "conditional generative adversarial and convolutional networks for x ray breast mass segmentation and shape classification"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"):
  with members:
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "dual path networks"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "densely connected 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)