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: "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
  Abstract: This report describes our submission to the ISIC 2017 Challenge in Skin Lesion Analysis Towards Melanoma Detection. We have participated in the Part 3: Lesion Classification with a system for automatic diagnosis of nevus, melanoma and seborrheic keratosis. Our approach aims to incorporate the expert knowledge of dermatologists into the well known framework of Convolutional Neural Networks (CNN), which have shown impressive performance in many visual recognition tasks. In particular, we have designed several networks providing lesion area identification, lesion segmentation into structural patterns and final diagnosis of clinical cases. Furthermore, novel blocks for CNNs have been designed to integrate this information with the diagnosis processing pipeline.

Connected through hyperedge HE-1 (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"
    - "data augmentation for skin lesion analysis"

Connected through hyperedge HE-2 (induced by source paper "a mutual bootstrapping model for automated skin lesion segmentation and classification"):
  with members:
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "dilated residual networks"

Connected through hyperedge HE-3 (induced by source paper "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "recod titans at isic challenge 2017"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "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-4 (induced by source paper "collaborative human ai chai evidence based interpretable melanoma classification in dermoscopic images"):
  with members:
    - "learning deep representations of medical images using siamese cnns with application to content based image retrieval"
    - "recod titans at isic challenge 2017"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "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"
    - "understanding neural networks through deep visualization"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "multiple convolutional neural network for skin dermoscopic image classification"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "recod titans at isic challenge 2017"
    - "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 "data depth and design learning reliable models for skin lesion analysis"):
  with members:
    - "data augmentation for skin lesion analysis"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "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"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "recod titans at isic challenge 2017"
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "lesion border detection in dermoscopy images"
    - "automatic detection of blue white veil and related structures in dermoscopy images"

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)