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 "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks"):
  Abstract: The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of atrophy and lesion burden, which are key criteria for the diagnosis, prognosis and longitudinal monitoring in MS. Achieving robust and reliable segmentation across multi-center spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework, robust to variability in both image parameters and clinical condition, for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data. 1042 adult subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-center study (n=30 centers). Data spanned 3 contrasts (T1-, T2- and T2*-weighted) for a total of 1943 volumes and featured large heterogeneity in terms of resolution, orientation, coverage and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a cascade of two Convolutional Neural Networks (CNN): a first CNN with 2D dilated convolutions detects the spinal cord centerline followed by a second 3D CNN that segments the spinal cord and lesions. When compared to a state-of-the-art spinal cord segmentation method (PropSeg), our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg. Regarding lesion segmentation, our framework, when compared with manual segmentation of MS patients, provided a lesion-wise detection sensitivity of 83%, a precision of 77%, a relative volume difference of 15%, and a Dice of 60%. The proposed framework is open-source and available in the Spinal Cord Toolbox.

Includes members:
  Member "multiple sclerosis lesion segmentation from brain mri via fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi branch convolutional neural network for multiple sclerosis lesion segmentation")
  Member "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "spinal cord gray matter segmentation using deep dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised domain adaptation for medical imaging segmentation with self ensembling")
  Member "domain adaptation for mri organ segmentation using reverse classification accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a modality adaptive method for segmenting brain tumors and organs at risk in radiation therapy planning")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field")
  Member "brain tumor segmentation with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "semi supervised deep learning for fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep co training for semi supervised image segmentation")

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)