human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "unsupervised shape and motion analysis of 3822 cardiac 4d mris of uk biobank"):
  Abstract: We perform unsupervised analysis of image-derived shape and motion features extracted from 3822 cardiac 4D MRIs of the UK Biobank. First, with a feature extraction method previously published based on deep learning models, we extract from each case 9 feature values characterizing both the cardiac shape and motion. Second, a feature selection is performed to remove highly correlated feature pairs. Third, clustering is carried out using a Gaussian mixture model on the selected features. After analysis, we identify two small clusters which probably correspond to two pathological categories. Further confirmation using a trained classification model and dimensionality reduction tools is carried out to support this discovery. Moreover, we examine the differences between the other large clusters and compare our measures with the ground-truth.

Includes members:
  Member "unsupervised segmentation of 3d medical images based on clustering and deep representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mumford shah loss functional for image segmentation with deep learning")
  Member "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis")
  Member "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking fully convolutional networks for the analysis of photoluminescence wafer images")
  Member "machine learning methods for histopathological image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi stream faster rcnn for mitosis counting in breast cancer images")
  Member "3d consistent biventricular myocardial segmentation using deep learning for mesh generation":
    also connected through other hyperedges:
      (none)
  Member "scikit learn machine learning in python":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scail classifier weights scaling for class incremental learning")

Question: Please tell me which class the source paper 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)