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: "deep supervision with additional labels for retinal vessel segmentation task"
  Abstract: Automatic analysis of retinal fundus images is of vital importance in diagnosis tasks of retinopathy. Segmenting vessels accurately is a fundamental step in analysing retinal images. However, it is usually difficult due to various imaging conditions, low image contrast and the appearance of pathologies such as micro-aneurysms. In this paper, we propose a novel method with deep neural networks to solve this problem. We utilize U-net with residual connection to detect vessels. To achieve better accuracy, we introduce an edge-aware mechanism, in which we convert the original task into a multi-class task by adding additional labels on boundary areas. In this way, the network will pay more attention to the boundary areas of vessels and achieve a better performance, especially in tiny vessels detecting. Besides, side output layers are applied in order to give deep supervision and therefore help convergence. We train and evaluate our model on three databases: DRIVE, STARE, and CHASEDB1. Experimental results show that our method has a comparable performance with AUC of 97.99% on DRIVE and an efficient running time compared to the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "embedded deep learning in ophthalmology making ophthalmic imaging smarter"
    - "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn"
    - "deep visual domain adaptation a survey"
    - "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning"
    - "deep learning based retinal oct segmentation"
    - "disc aware ensemble network for glaucoma screening from fundus image"
    - "a deep learning based joint segmentation and classification framework for glaucoma assesment in retinal color fundus images"

Connected through hyperedge HE-2 (induced by source paper "graph attention network based pruning for reconstructing 3d liver vessel morphology from contrasted ct images"):
  with members:
    - "graph neural network for interpreting task fmri biomarkers"
    - "on the convergence of adam and beyond"
    - "fast graph representation learning with pytorch geometric"
    - "deep vessel segmentation by learning graphical connectivity"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "a two stream meticulous processing network for retinal vessel segmentation"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "deep retinal image understanding"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "ace net biomedical image segmentation with augmented contracting and expansive paths"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "learning steerable filters for rotation equivariant cnns"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation"
    - "the importance of skip connections in biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation"):
  with members:
    - "structure measure a new way to evaluate foreground maps"
    - "deep retinal image understanding"
    - "holistically nested edge detection"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"
    - "retinal vessel segmentation using the 2 d gabor wavelet and supervised classification"

Connected through hyperedge HE-6 (induced by source paper "residual pyramid fcn for robust follicle segmentation"):
  with members:
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "slsdeep skin lesion segmentation based on dilated residual and pyramid pooling networks"
    - "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-7 (induced by source paper "deep vessel segmentation by learning graphical connectivity"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "iterative deep learning for road topology extraction"
    - "videos as space time region graphs"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "extraction of airways using graph neural networks"
    - "situation recognition with graph neural networks"
    - "deformable shape completion with graph convolutional autoencoders"
    - "feastnet feature steered graph convolutions for 3d shape analysis"

Connected through hyperedge HE-8 (induced by source paper "patchfcn for intracranial hemorrhage detection"):
  with members:
    - "autofocus layer for semantic segmentation"
    - "dilated residual networks"
    - "application of deep learning in neuroradiology automated detection of basal ganglia hemorrhage using 2d convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

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)