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: "deep learning ensembles for melanoma recognition in dermoscopy images"
  Abstract: Melanoma is the deadliest form of skin cancer. While curable with early detection, only highly trained specialists are capable of accurately recognizing the disease. As expertise is in limited supply, automated systems capable of identifying disease could save lives, reduce unnecessary biopsies, and reduce costs. Toward this goal, we propose a system that combines recent developments in deep learning with established machine learning approaches, creating ensembles of methods that are capable of segmenting skin lesions, as well as analyzing the detected area and surrounding tissue for melanoma detection. The system is evaluated using the largest publicly available benchmark dataset of dermoscopic images, containing 900 training and 379 testing images. New state-of-the-art performance levels are demonstrated, leading to an improvement in the area under receiver operating characteristic curve of 7.5% (0.843 vs. 0.783), in average precision of 4% (0.649 vs. 0.624), and in specificity measured at the clinically relevant 95% sensitivity operating point 2.9 times higher than the previous state-of-the-art (36.8% specificity compared to 12.5%). Compared to the average of 8 expert dermatologists on a subset of 100 test images, the proposed system produces a higher accuracy (76% vs. 70.5%), and specificity (62% vs. 59%) evaluated at an equivalent sensitivity (82%).

Connected through hyperedge HE-1 (induced by source paper "assessing robustness of deep learning methods in dermatological workflow"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "pytorch an imperative style high performance deep learning library"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "sgdr stochastic gradient descent with restarts"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "visual diagnosis of dermatological disorders human and machine performance"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "diagnostic accuracy of content based dermatoscopic image retrieval with deep classification features"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "knowledge transfer for melanoma screening with deep 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"
    - "compact bilinear pooling"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "supervised saliency map driven segmentation of lesions in dermoscopic images"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "salient object detection a discriminative regional feature integration approach"
    - "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"
    - "salient object detection a benchmark"
    - "what is a salient object a dataset and a baseline model for salient object detection"
    - "lesion border detection in dermoscopy images using ensembles of thresholding methods"

Connected through hyperedge HE-5 (induced by source paper "automated skin lesion classification using ensemble of deep neural networks in isic 2018 skin lesion analysis towards melanoma detection challenge"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "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"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "estimating skin tone and effects on classification performance in dermatology datasets"):
  with members:
    - "diversity in faces"
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "predictive inequity in object detection"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "mask r cnn"

Connected through hyperedge HE-7 (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"
    - "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-8 (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)