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: "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation"
  Abstract: Abstract   Background and objective  The condition of vessel of the human eye is an important factor for the diagnosis of ophthalmological diseases. Vessel segmentation in fundus images is a challenging task due to complex vessel structure, the presence of similar structures such as microaneurysms and hemorrhages, micro-vessel with only one to several pixels wide, and requirements for finer results.    Methods  In this paper, we present a multi-scale deeply supervised network with short connections (BTS-DSN) for vessel segmentation. We used short connections to transfer semantic information between side-output layers. Bottom-top short connections pass low level semantic information to high level for refining results in high-level side-outputs, and top-bottom short connection passes much structural information to low level for reducing noises in low-level side-outputs. In addition, we employ cross-training to show that our model is suitable for real world fundus images.    Results  The proposed BTS-DSN has been verified on DRIVE, STARE and CHASE_DB1 datasets, and showed competitive performance over other state-of-the-art methods. Specially, with patch level input, the network achieved 0.7891/0.8212 sensitivity, 0.9804/0.9843 specificity, 0.9806/0.9859 AUC, and 0.8249/0.8421 F1-score on DRIVE and STARE, respectively. Moreover, our model behaves better than other methods in cross-training experiments.    Conclusions  BTS-DSN achieves competitive performance in vessel segmentation task on three public datasets. It is suitable for vessel segmentation. The source code of our method is available at:  https://github.com/guomugong/BTS-DSN .

Connected through hyperedge HE-1 (induced by source paper "active learning for segmentation based on bayesian sample queries"):
  with members:
    - "variational adversarial active learning"
    - "geometry in active learning for binary and multi class image segmentation"
    - "active learning for segmentation by optimizing content information for maximal entropy"
    - "learn the new keep the old extending pretrained models with new anatomy and images"
    - "deep bayesian active semi supervised learning"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep bayesian active learning with image data"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps"):
  with members:
    - "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "understanding autoencoders with information theoretic concepts"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "brain tumor segmentation with deep neural networks"
    - "cell detection in microscopy images with deep convolutional neural network and compressed sensing"
    - "opening the black box of deep neural networks via information"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

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)