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: "et net a generic edge attention guidance network for medical image segmentation"
  Abstract: Segmentation is a fundamental task in medical image analysis. However, most existing methods focus on primary region extraction and ignore edge information, which is useful for obtaining accurate segmentation. In this paper, we propose a generic medical segmentation method, called Edge-aTtention guidance Network (ET-Net), which embeds edge-attention representations to guide the segmentation network. Specifically, an edge guidance module is utilized to learn the edge-attention representations in the early encoding layers, which are then transferred to the multi-scale decoding layers, fused using a weighted aggregation module. The experimental results on four segmentation tasks (i.e., optic disc/cup and vessel segmentation in retinal images, and lung segmentation in chest X-Ray and CT images) demonstrate that preserving edge-attention representations contributes to the final segmentation accuracy, and our proposed method outperforms current state-of-the-art segmentation methods. The source code of our method is available at this https URL.

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"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "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 "hierarchical attention networks for medical image segmentation"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "expectation maximization attention networks for semantic segmentation"
    - "patch based output space adversarial learning for joint optic disc and cup segmentation"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "a simple pooling based design for real time salient object detection"
    - "ce net context encoder network for 2d medical image segmentation"
    - "fast interactive object annotation with curve gcn"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "a deep gradient boosting network for optic disc and cup segmentation"):
  with members:
    - "ce net context encoder network for 2d medical image segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "deformable convolutional networks"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "image augmentation using radial transform for training deep neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)