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: "knowledge transfer for melanoma screening with deep learning"
  Abstract: Knowledge transfer impacts the performance of deep learning — the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art on automated melanoma screening employs some form of transfer learning, a systematic evaluation was missing. Here we investigate the presence of transfer, from which task the transfer is sourced, and the application of fine tuning (i.e., retraining of the deep learning model after transfer). We also test the impact of picking deeper (and more expensive) models. Our results favor deeper models, pretrained over ImageNet, with fine-tuning, reaching an AUC of 80.7% and 84.5% for the two skin-lesion datasets evaluated.

Connected through hyperedge HE-1 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "self supervised learning for cardiac mr image segmentation by anatomical position prediction"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "nnu net self adapting framework for u net based medical image segmentation"

Connected through hyperedge HE-2 (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-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"
    - "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"
    - "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 "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"
    - "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"
    - "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-5 (induced by source paper "cats or cat scans transfer learning from natural or medical image source data sets"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "is the winner really the best a critical analysis of common research practice in biomedical image analysis competitions"
    - "building medical image classifiers with very limited data using segmentation networks"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (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"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "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-7 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

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"
    - "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"
    - "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)