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 "automated brain extraction of multisequence mri using artificial neural networks"):
  Abstract: Brain extraction is a critical preprocessing step in the analysis of MRI neuroimaging studies and influences the accuracy of downstream analyses. The majority of brain extraction algorithms are, however, optimized for processing healthy brains and thus frequently fail in the presence of pathologically altered brain or when applied to heterogeneous MRI datasets. Here we introduce a new, rigorously validated algorithm (termed HD-BET) relying on artificial neural networks that aims to overcome these limitations. We demonstrate that HD-BET outperforms six popular, publicly available brain extraction algorithms in several large-scale neuroimaging datasets, including one from a prospective multicentric trial in neuro-oncology, yielding state-of-the-art performance with median improvements of +1.16 to +2.11 points for the DICE coefficient and -0.66 to -2.51 mm for the Hausdorff distance. Importantly, the HD-BET algorithm shows robust performance in the presence of pathology or treatment-induced tissue alterations, is applicable to a broad range of MRI sequence types and is not influenced by variations in MRI hardware and acquisition parameters encountered in both research and clinical practice. For broader accessibility our HD-BET prediction algorithm is made freely available (this http URL) and may become an essential component for robust, automated, high-throughput processing of MRI neuroimaging data.

Includes members:
  Member "brain tumor segmentation and radiomics survival prediction contribution to the brats 2017 challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "deepneuro an open source deep learning toolbox for neuroimaging":
    also connected through other hyperedges:
      (none)
  Member "compnet complementary segmentation network for brain mri extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation")
  Member "brain extraction from normal and pathological images a joint pca image reconstruction approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compnet complementary segmentation network for brain mri extraction")
  Member "data augmentation instead of explicit regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data augmentation with mobius transformations")
  Member "imperfect segmentation labels how much do they matter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving training on noisy stuctured labels")
  Member "cnn based segmentation of medical imaging data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low memory neural network training a technical report")
  Member "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks")

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)