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 driven color augmentation techniques for deep skin image analysis"
  Abstract: Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color normalization, we propose to leverage computational color constancy techniques to build an artificial data augmentation technique suitable for this kind of images. Specifically, we apply the \emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants. We then draw one sample from the distribution of training set illuminants and apply it on the normalized image. We employ this technique for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesion classification, in the context of the ISIC 2017 challenge and without using any external dermatologic image set. Our results on the validation set are promising, and will be supplemented with extended results on the hidden test set when available.

Connected through hyperedge HE-1 (induced by source paper "style transfer based image synthesis as an efficient regularization technique in deep learning"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "text to image synthesis using generative adversarial networks"
    - "staingan stain style transfer for digital histological images"
    - "gans for medical image analysis"
    - "random erasing data augmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"

Connected through hyperedge HE-2 (induced by source paper "adapting semantic segmentation models for changes in illumination and camera perspective"):
  with members:
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "regularized deep networks in intelligent transportation systems a taxonomy and a case study"):
  with members:
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "exploring the landscape of spatial robustness"
    - "fractalnet ultra deep neural networks without residuals"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "an overview of gradient descent optimization algorithms"
    - "robust convolutional neural networks under adversarial noise"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "segmentation of skin lesions and their attributes using multi scale convolutional neural networks and domain specific augmentations"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "on the convergence of adam and beyond"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "a detection and segmentation architecture for skin lesion segmentation on dermoscopy images"
    - "automatic skin lesion segmentation using deep fully convolutional networks"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling 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)