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 and quantification from cardiac cine mr images via multi task learning"
  Abstract: Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of $0.849 \pm 0.036$ and mean surface distance of $0.274 \pm 0.083$ mm, while simultaneously estimating the myocardial area with mean absolute difference error of $205\pm198$ mm$^2$.

Connected through hyperedge HE-1 (induced by source paper "a distance map regularized cnn for cardiac cine mr image segmentation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"
    - "a survey on deep learning in medical image analysis"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"

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 via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "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"

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)