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: "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
  Abstract: Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than $100$ images of size 384x384 per second on a recent GPU.

Connected through hyperedge HE-1 (induced by source paper "complementary network with adaptive receptive fields for melanoma segmentation"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional 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-2 (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"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "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-3 (induced by source paper "non local context encoder robust biomedical image segmentation against adversarial attacks"):
  with members:
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "contrast oriented deep neural networks for salient object detection"
    - "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-4 (induced by source paper "residual pyramid fcn for robust follicle segmentation"):
  with members:
    - "deep supervision with additional labels for retinal vessel segmentation task"
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "dilated residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (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"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "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"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-6 (induced by source paper "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"):
  with members:
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "icadx interpretable computer aided diagnosis of breast masses"
    - "retinal optic disc segmentation using conditional generative adversarial network"
    - "conditional generative adversarial and convolutional networks for x ray breast mass segmentation and shape classification"
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "a survey on deep learning in medical image analysis"
    - "deep learning with topological signatures"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "a multi task framework for skin lesion detection and segmentation"):
  with members:
    - "skinnet a deep learning framework for skin lesion segmentation"
    - "a novel multi task deep learning model for skin lesion segmentation and classification"
    - "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"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "automatic skin lesion segmentation using grabcut in hsv colour space"):
  with members:
    - "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)