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: "denoising adversarial autoencoders classifying skin lesions using limited labelled training data"
  Abstract: We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in limited supply. We consider the specific case of classifying skin lesions as either malignant or benign. In this setting, the proposed approach -- the semi-supervised, denoising adversarial autoencoder -- is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. We analyse the contributions of both the adversarial and denoising components of the model and find that the combination yields superior classification performance in the setting of limited labelled training data.

Connected through hyperedge HE-1 (induced by source paper "semantic denoising autoencoders for retinal optical coherence tomography"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a dataset of laryngeal endoscopic images with comparative study on convolution neural network based semantic segmentation"):
  with members:
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "focal loss for dense object detection"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"

Connected through hyperedge HE-3 (induced by source paper "anomaly detection for skin disease images using variational autoencoder"):
  with members:
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved training of wasserstein gans"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "pixelvae a latent variable model for natural images"
    - "importance weighted autoencoders"
    - "unsupervised representation learning with deep convolutional generative adversarial 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)