human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "de constructing bias on skin lesion datasets"):
  Abstract: Melanoma is the deadliest form of skin cancer. Automated skin lesion analysis plays an important role for early detection. Nowadays, the ISIC Archive and the Atlas of Dermoscopy dataset are the most employed skin lesion sources to benchmark deep-learning based tools. However, all datasets contain biases, often unintentional, due to how they were acquired and annotated. Those biases distort the performance of machine-learning models, creating spurious correlations that the models can unfairly exploit, or, contrarily destroying cogent correlations that the models could learn. In this paper, we propose a set of experiments that reveal both types of biases, positive and negative, in existing skin lesion datasets. Our results show that models can correctly classify skin lesion images without clinically-meaningful information: disturbingly, the machine-learning model learned over images where no information about the lesion remains, presents an accuracy above the AI benchmark curated with dermatologists' performances. That strongly suggests spurious correlations guiding the models. We fed models with additional clinically meaningful information, which failed to improve the results even slightly, suggesting the destruction of cogent correlations. Our main findings raise awareness of the limitations of models trained and evaluated in small datasets such as the ones we evaluated, and may suggest future guidelines for models intended for real-world deployment.

Includes members:
  Member "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical and bilateral privacy preserving federated learning")
  Member "skin lesion synthesis with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "syncgan using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images")
  Member "data augmentation for skin lesion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "class dependent compression of deep neural networks")
  Member "data depth and design learning reliable models for melanoma screening":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "data depth and design learning reliable models for skin lesion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "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":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  Member "an overview of melanoma detection in dermoscopy images using image processing and machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual diagnosis of dermatological disorders human and machine performance")

Question: Please tell me which class the source paper 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)