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: "towards automated melanoma screening proper computer vision reliable results"
  Abstract: In this paper we survey, analyze and criticize current art on automated melanoma screening, reimplementing a baseline technique, and proposing two novel ones. Melanoma, although highly curable when detected early, ends as one of the most dangerous types of cancer, due to delayed diagnosis and treatment. Its incidence is soaring, much faster than the number of trained professionals able to diagnose it. Automated screening appears as an alternative to make the most of those professionals, focusing their time on the patients at risk while safely discharging the other patients. However, the potential of automated melanoma diagnosis is currently unfulfilled, due to the emphasis of current literature on outdated computer vision models. Even more problematic is the irreproducibility of current art. We show how streamlined pipelines based upon current Computer Vision outperform conventional models — a model based on an advanced bags of words reaches an AUC of 84.6%, and a model based on deep neural networks reaches 89.3%, while the baseline (a classical bag of words) stays at 81.2%. We also initiate a dialog to improve reproducibility in our community.

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 "handling inter annotator agreement for automated skin lesion segmentation"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "data augmentation for skin lesion analysis"
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"
    - "knowledge transfer for melanoma screening with deep learning"
    - "an empirical study into annotator agreement ground truth estimation and algorithm evaluation"
    - "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-3 (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"
    - "recod titans at isic challenge 2017"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"

Connected through hyperedge HE-4 (induced by source paper "skin lesion synthesis with generative adversarial networks"):
  with members:
    - "data augmentation for skin lesion analysis"
    - "data depth and design learning reliable models for melanoma screening"
    - "progressive growing of gans for improved quality stability and variation"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "recod titans at isic challenge 2017"
    - "inception v4 inception resnet and the impact of residual connections on 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"

Connected through hyperedge HE-5 (induced by source paper "data augmentation for skin lesion analysis"):
  with members:
    - "skin lesion synthesis with generative adversarial networks"
    - "data depth and design learning reliable models for melanoma screening"
    - "random erasing data augmentation"
    - "knowledge transfer for melanoma screening with deep learning"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "image classification of melanoma nevus and seborrheic keratosis by deep neural network ensemble"
    - "inception v4 inception resnet and the impact of residual connections on 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"

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"
    - "incorporating the knowledge of dermatologists to convolutional neural networks for the diagnosis of skin lesions"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-7 (induced by source paper "knowledge transfer for melanoma screening with deep learning"):
  with members:
    - "deep learning ensembles for melanoma recognition in dermoscopy images"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer 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"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "recod titans at isic challenge 2017"):
  with members:
    - "knowledge transfer for melanoma screening with deep learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)