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 "hierarchical multi class segmentation of glioma images using networks with multi level activation function"):
  Abstract: For many segmentation tasks, especially for the biomedical image, the topological prior is vital information which is useful to exploit. The containment/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes 'whole tumor', 'tumor core', 'active tumor', the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network comprises a context aggregation pathway and a localization pathway, which encodes increasingly abstract representation of the input as going deeper into the network, and then recombines these representations with shallower features to precisely localize the interest domain via a localization path. The nested-class-prior is combined by proposing the multi-class activation function and its corresponding loss function. The model is trained on the training dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tumor, enhancing tumor and tumor core classes without relying on ensembles or complicated post-processing steps. Based on the same start-of-the-art network architecture, the accuracy of nested-class (enhancing tumor) is reasonably improved from 69% to 72% compared with the traditional Softmax-based method which blind to topological prior.

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 "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical image segmentation")
  Member "multi level activation for segmentation of hierarchically nested classes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepvesselnet vessel segmentation centerline prediction and bifurcation detection in 3 d angiographic volumes")
  Member "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")
  Member "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review")
  Member "deep learning markov random field for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical 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)