human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "skin lesion synthesis with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "syncgan using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images")
  Member "data depth and design learning reliable models for melanoma screening":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on feature normalization and data augmentation")
  Member "knowledge transfer for melanoma screening with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "towards automated melanoma screening proper computer vision reliable results":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")

Question: Please tell me which class the source paper 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)