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: "data depth and design learning reliable models for melanoma screening"
  Abstract: State of the art on melanoma screening evolved rapidly in the last two years, with the adoption of deep learning. Those models, however, pose challenges of their own, as they are expensive to train and difficult to parameterize. Objective: We investigate the methodological issues for designing and evaluating deep learning models for melanoma screening, by exploring nine choices often faced to design deep networks: model architecture, training dataset, image resolution, type of data augmentation, input normalization, use of segmentation, duration of training, additional use of SVM, and test data augmentation. Methods: We perform a two-level full factorial experiment, for five different test datasets, resulting in 2560 exhaustive trials, which we analyze using a multi-way ANOVA. Results: The main finding is that the size of training data has a disproportionate influence, explaining almost half the variation in performance. Of the other factors, test data augmentation and input resolution are the most helpful. Deeper models, when combined, with extra data, also help. We show that the costly full factorial design, or the unreliable sequential optimization, are not the only options: ensembles of models provide reliable results with limited resources. Conclusions and Significance: To move research forward on automated melanoma screening, we need to curate larger shared datasets. Optimizing hyperparameters and measuring performance on the same dataset is common, but leads to overoptimistic results. Ensembles of models are a cost-effective alternative to the expensive full-factorial and to the unstable sequential designs.

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 "de constructing bias on skin lesion datasets"):
  with members:
    - "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic"
    - "skin lesion synthesis with generative adversarial networks"
    - "data augmentation for skin lesion analysis"
    - "data depth and design learning reliable models for skin lesion analysis"
    - "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"
    - "an overview of melanoma detection in dermoscopy images using image processing and machine learning"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "skin lesion synthesis with generative adversarial networks"):
  with members:
    - "data augmentation for skin lesion analysis"
    - "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"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "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-4 (induced by source paper "data augmentation for skin lesion analysis"):
  with members:
    - "skin lesion synthesis with generative adversarial networks"
    - "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"
    - "towards automated melanoma screening proper computer vision reliable results"
    - "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"

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)