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 "region of interest detection in dermoscopic images for natural data augmentation"):
  Abstract: With the rapid growth of medical imaging research, there is a great interest in the automated detection of skin lesions with computer algorithms. The state-of-the-art datasets for skin lesions are often accompanied with very limited amount of ground truth labeling as it is laborious and expensive. The region of interest (ROI) detection is vital to locate the lesion accurately and robust to subtle features of different skin lesion types. In this work, we propose the use of two object localization meta-architectures for end-to-end ROI skin lesion detection in dermoscopic images. We trained the Faster-RCNN-InceptionV2 and SSD-InceptionV2 on ISBI-2017 training dataset and evaluate the performances on ISBI-2017 testing set, PH2 and HAM10000 datasets. Since there was no earlier work in ROI detection for skin lesion with CNNs, we compare the performance of skin localization methods with the state-of-the-art segmentation method. The localization methods proved superiority over the segmentation method in ROI detection on skin lesion datasets. In addition, based on the detected ROI, an automated natural data-augmentation method is proposed. To demonstrate the potential of our work, we developed a real-time mobile application for automated skin lesions detection. The codes and mobile application will be made available for further research purposes.

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 "multi class semantic segmentation of skin lesions via fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic segmentation of human thigh quadriceps muscle in magnetic resonance images")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "speed accuracy trade offs for modern convolutional object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")
  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-6 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation")

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)