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: "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
  Abstract: Automatic skin lesion segmentation on dermoscopic images is an essential step in computer-aided diagnosis of melanoma. However, this task is challenging due to significant variations of lesion appearances across different patients. This challenge is further exacerbated when dealing with a large amount of image data. In this paper, we extended our previous work by developing a deeper network architecture with smaller kernels to enhance its discriminant capacity. In addition, we explicitly included color information from multiple color spaces to facilitate network training and thus to further improve the segmentation performance. We participated and extensively evaluated our method on the ISBI 2017 skin lesion segmentation challenge. By training with the 2000 challenge training images, our method achieved an average Jaccard Index of 0.765 on the 600 challenge testing images, which ranked itself in the first place among 21 final submissions in the challenge.

Connected through hyperedge HE-1 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "feature boosting network for 3d pose estimation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "boundary aware feature propagation for scene segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"

Connected through hyperedge HE-2 (induced by source paper "complementary network with adaptive receptive fields for melanoma segmentation"):
  with members:
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "focal loss for dense object detection"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "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"
    - "u net convolutional networks for biomedical image segmentation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "a mutual bootstrapping model for automated skin lesion segmentation and classification"):
  with members:
    - "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "dilated residual networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"

Connected through hyperedge HE-4 (induced by source paper "non local context encoder robust biomedical image segmentation against adversarial attacks"):
  with members:
    - "contrast oriented deep neural networks for salient object detection"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "context encoding for semantic segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "accurate lung segmentation via network wise training of convolutional networks"
    - "instance level salient object segmentation"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-5 (induced by source paper "deep convolutional encoder decoders with aggregated multi resolution skip connections for skin lesion segmentation"):
  with members:
    - "deep learning ensembles for skin lesion segmentation analysis classification recod titans at isic challenge 2018"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "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"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "segmentation of skin lesions and their attributes using multi scale convolutional neural networks and domain specific augmentations"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "on the convergence of adam and beyond"
    - "a detection and segmentation architecture for skin lesion segmentation on dermoscopy images"
    - "automatic skin lesion segmentation using deep fully convolutional networks"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-7 (induced by source paper "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"):
  with members:
    - "skin lesion segmentation u nets versus clustering"
    - "dilated residual networks"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"

Connected through hyperedge HE-8 (induced by source paper "star shape prior in fully convolutional networks for skin lesion segmentation"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "understanding convolution for semantic segmentation"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "automatic skin lesion analysis using large scale dermoscopy images and deep residual networks"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "incorporating prior knowledge in medical image segmentation a survey"

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)