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: "data augmentation for skin lesion analysis"
  Abstract: Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.

Connected through hyperedge HE-1 (induced by source paper "class dependent compression of deep neural networks"):
  with members:
    - "knowledge squeezed adversarial network compression"
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "exploring randomly wired neural networks for image recognition"
    - "discovering neural wirings"
    - "class balanced loss based on effective number of samples"
    - "darc differentiable architecture compression"
    - "compressing gans using knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "skin lesion analysis towards melanoma detection via end to end deep learning of convolutional neural networks"
    - "ensemble of convolutional neural networks for dermoscopic images classification"
    - "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting"
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"

Connected through hyperedge HE-3 (induced by source paper "semi supervised and task driven data augmentation"):
  with members:
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "pathology segmentation using distributional differences to images of healthy origin"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "learning to segment medical images with scribble supervision alone"
    - "mixup beyond empirical risk minimization"
    - "semantic segmentation using adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "de constructing bias on skin lesion datasets"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "skin lesion synthesis with generative adversarial networks"
    - "data depth and design learning reliable models for melanoma screening"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "handling inter annotator agreement for automated skin lesion segmentation"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"
    - "knowledge transfer for melanoma screening with deep learning"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "an empirical study into annotator agreement ground truth estimation and algorithm evaluation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "skin lesion synthesis with generative adversarial networks"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "knowledge transfer for melanoma screening with deep learning"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "recod titans at isic challenge 2017"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "towards automated melanoma screening proper computer vision reliable results"

Connected through hyperedge HE-7 (induced by source paper "skin lesion synthesis with generative adversarial networks"):
  with members:
    - "data depth and design learning reliable models for melanoma screening"
    - "progressive growing of gans for improved quality stability and variation"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "recod titans at isic challenge 2017"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "data depth and design learning reliable models for skin lesion analysis"):
  with members:
    - "skin lesion classification using deep multi scale convolutional neural networks"
    - "skin lesion detection based on an ensemble of deep convolutional neural network"
    - "knowledge transfer for melanoma screening with deep learning"
    - "skin lesion classification using class activation map"
    - "a novel multi task deep learning model for skin lesion segmentation and classification"
    - "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
    - "a survey on deep learning in medical image analysis"
    - "convolutional neural network committees for melanoma classification with classical and expert knowledge based image transforms data augmentation"

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)