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: "recod titans at isic challenge 2017"
  Abstract: This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017). Although our team has a long experience with melanoma classification, the ISIC Challenge 2017 was the very first time we worked on skin-lesion segmentation. For part 1 (segmentation), our final submission used four of our models: two trained with all 2000 samples, without a validation split, for 250 and for 500 epochs respectively; and other two trained and validated with two different 1600/400 splits, for 220 epochs. Those four models, individually, achieved between 0.780 and 0.783 official validation scores. Our final submission averaged the output of those four models achieved a score of 0.793. For part 3 (classification), the submitted test run as well as our last official validation run were the result from a meta-model that assembled seven base deep-learning models: three based on Inception-V4 trained on our largest dataset; three based on Inception trained on our smallest dataset; and one based on ResNet-101 trained on our smaller dataset. The results of those component models were stacked in a meta-learning layer based on an SVM trained on the validation set of our largest dataset.

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 "towards automated melanoma detection with deep learning data purification and augmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "knowledge transfer for melanoma screening with deep learning"
    - "convolutional neural network committees for melanoma classification with classical and expert knowledge based image transforms data augmentation"
    - "skin lesion analysis towards melanoma detection using deep learning network"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "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"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"
    - "theano a python framework for fast computation of mathematical expressions"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional random fields as recurrent neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (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"
    - "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
    - "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-6 (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"

Connected through hyperedge HE-7 (induced by source paper "the effects of image pre and post processing wavelet decomposition and local binary patterns on u nets for skin lesion segmentation"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "skin lesion segmentation u nets versus clustering"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "spatially aware melanoma segmentation using hybrid deep learning techniques"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (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"
    - "data augmentation for skin lesion analysis"
    - "knowledge transfer for melanoma screening with deep learning"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "towards automated melanoma screening proper computer vision reliable results"

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)