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 "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"):
  Abstract: This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018). Although our team has a long experience with melanoma classification and moderate experience with lesion segmentation, the ISIC Challenge 2018 was the very first time we worked on lesion attribute detection. For each task we submitted 3 different ensemble approaches, varying combinations of models and datasets. Our best results on the official testing set, regarding the official metric of each task, were: 0.728 (segmentation), 0.344 (attribute detection) and 0.803 (classification). Those submissions reached, respectively, the 56th, 14th and 9th places.

Includes members:
  Member "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  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 "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "data augmentation for skin lesion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "class dependent compression of deep neural networks")
  Member "knowledge transfer for melanoma screening with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "data depth and design learning reliable models for skin lesion analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "de constructing bias on skin lesion datasets")
  Member "recod titans at isic challenge 2017":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "solo or ensemble choosing a cnn architecture for melanoma classification")
  Member "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy")

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)