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: "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
  Abstract: Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of LV provides essential information on the contracting/relaxing pattern of heart, which is keenly evaluated by clinical experts in clinical practice. Inspired by the expert way of viewing Cine MRI, we propose a new CNN module that is able to incorporate the temporal information into LV segmentation from cine MRI. In the proposed CNN, the optical flow (OF) between neighboring frames is integrated and aggregated at feature level, such that temporal coherence in cardiac motion can be taken into account during segmentation. The proposed module is integrated into the U-net architecture without need of additional training. Furthermore, dilated convolution is introduced to improve the spatial accuracy of segmentation. Trained and tested on the Cardiac Atlas database, the proposed network resulted in a Dice index of 95% and an average perpendicular distance of 0.9 pixels for the middle LV contour, significantly outperforming the original U-net that processes each frame individually. Notably, the proposed method improved the temporal coherence of LV segmentation results, especially at the LV apex and base where the cardiac motion is difficult to follow.

Connected through hyperedge HE-1 (induced by source paper "a spatiotemporal volumetric interpolation network for 4d dynamic medical image"):
  with members:
    - "an unsupervised learning model for deformable medical image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-2 (induced by source paper "tan temporal affine network for real time left ventricle anatomical structure analysis based on 2d ultrasound videos"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "a comprehensive approach for learning based fully automated inter slice motion correction for short axis cine cardiac mr image stacks"
    - "left ventricle segmentation and quantification from cardiac cine mr images via multi task learning"
    - "iterative segmentation from limited training data applications to congenital heart disease"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "automated segmentation on the entire cardiac cycle using a deep learning work flow"
    - "ai challenger a large scale dataset for going deeper in image understanding"

Connected through hyperedge HE-3 (induced by source paper "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "enhancing label driven deep deformable image registration with local distance metrics for state of the art cardiac motion tracking"
    - "joint learning of motion estimation and segmentation for cardiac mr image sequences"
    - "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers"
    - "learning based regularization for cardiac strain analysis with ability for domain adaptation"
    - "unsupervised probabilistic deformation modeling for robust diffeomorphic registration"

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)