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: "the deep poincar e map a novel approach for left ventricle segmentation"
  Abstract: Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the the image, tracing out a boundary of the ROI -- using the magnitude difference of the Poincar\'e map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.

Connected through hyperedge HE-1 (induced by source paper "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"):
  with members:
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "dilated residual networks"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri"
    - "deeply recursive convolutional network for image super resolution"
    - "image super resolution using deep convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised annotation of phenotypic abnormalities via semantic latent representations on electronic health records"):
  with members:
    - "integrating semantic knowledge to tackle zero shot text classification"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "attention is all you need"
    - "i2t2i learning text to image synthesis with textual data augmentation"
    - "towards automated icd coding using deep learning"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"

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)