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 "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"):
  Abstract: Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks, we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net. Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.

Includes members:
  Member "hyperdense net a hyper densely connected cnn for multi modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "ensembles of multiple models and architectures for robust brain tumour segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "suggestive annotation a deep active learning framework for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforced active learning for image segmentation")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "medgan medical image translation using gans")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "medgan medical image translation using gans")
  Member "3d fully convolutional networks for subcortical segmentation in mri a large scale study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semi supervised few shot learning for medical image segmentation")
  Member "identity mappings in deep residual networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep 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)