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 "one pass multi task networks with cross task guided attention for brain tumor segmentation"):
  Abstract: Class imbalance has emerged as one of the major challenges for medical image segmentation. The model cascade (MC) strategy significantly alleviates the class imbalance issue via running a set of individual deep models for coarse-to-fine segmentation. Despite its outstanding performance, however, this method leads to undesired system complexity and also ignores the correlation among the models. To handle these flaws, we propose a light-weight deep model, i.e., the One-pass Multi-task Network (OM-Net) to solve class imbalance better than MC does, while requiring only one-pass computation. First, OM-Net integrates the separate segmentation tasks into one deep model, which consists of shared parameters to learn joint features, as well as task-specific parameters to learn discriminative features. Second, to more effectively optimize OM-Net, we take advantage of the correlation among tasks to design both an online training data transfer strategy and a curriculum learning-based training strategy. Third, we further propose sharing prediction results between tasks and design a cross-task guided attention (CGA) module which can adaptively recalibrate channel-wise feature responses based on the category-specific statistics. Finally, a simple yet effective post-processing method is introduced to refine the segmentation results. Extensive experiments are conducted to demonstrate the effectiveness of the proposed techniques. Most impressively, we achieve state-of-the-art performance on the BraTS 2015 testing set and BraTS 2017 online validation set. Using these proposed approaches, we also won joint third place in the BraTS 2018 challenge among 64 participating teams. The code is publicly available at https://github.com/chenhong-zhou/OM-Net.

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 "harmonious attention network for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "adaptive feature recombination and recalibration for semantic segmentation application to brain tumor segmentation in mri":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a volumetric convolutional neural network for brain tumor segmentation")
  Member "3d mri brain tumor segmentation using autoencoder regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boosting segmentation with weak supervision from image to image translation")
  Member "compnet complementary segmentation network for brain mri extraction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automated brain extraction of multisequence mri using artificial neural networks")
  Member "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge based analysis for mortality prediction from ct images")
  Member "no new net":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multimodal self supervised learning for medical 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-8 (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical 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)