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: "multi views fusion cnn for left ventricular volumes estimation on cardiac mr images"
  Abstract: Objective : Left ventricular (LV) volume estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address a direct LV volume prediction task.  Methods : In this paper, we propose a direct volume prediction method based on the end-to-end deep convolutional neural networks. We study the end-to-end LV volume prediction method in items of the data preprocessing, network structure, and multiview fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new network structure for end-to-end LV volume estimation. Third, we explore the representational capacity of different slices and propose a fusion strategy to improve the prediction accuracy.  Results : The evaluation results show that the proposed method outperforms other state-of-the-art LV volume estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth (   ${\rm{EDV:R}}^{{\rm 2}}={\text{0.974}}$  ,    ${\rm{RMSE\,}}= {\text{9.6}}{\rm{\,ml}}$  ;    ${\rm{ESV:R}}^{{\rm 2}}={\text{0.976}}$  ,    ${\rm{RMSE}}= {\text{7.1}}\,{\text{ml}}$  ;    ${\rm{EF:R}}^{{\rm 2}} ={\text{0.828}}$  ,    ${\rm{RMSE}}= {\text{4.71}}\% $  ).  Conclusion : Experimental results prove that the proposed method may be useful for the LV volume prediction task.   Significance : The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fields.

Connected through hyperedge HE-1 (induced by source paper "a two stage 3d unet framework for multi class segmentation on full resolution image"):
  with members:
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "a self aware sampling scheme to efficiently train fully convolutional networks for semantic segmentation"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep learning for multi task medical image segmentation in multiple modalities"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast r cnn"
    - "deeply recursive convolutional network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "left ventricle segmentation and volume estimation on cardiac mri using deep learning"):
  with members:
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a fully convolutional neural network for cardiac segmentation in short axis mri"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "conditional generative adversarial nets"

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)