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 "ivd net intervertebral disc localization and segmentation in mri with a multi modal unet"):
  Abstract: Accurate localization and segmentation of intervertebral disc (IVD) is crucial for the assessment of spine disease diagnosis. Despite the technological advances in medical imaging, IVD localization and segmentation are still manually performed, which is time-consuming and prone to errors. If, in addition, multi-modal imaging is considered, the burden imposed on disease assessments increases substantially. In this paper, we propose an architecture for IVD localization and segmentation in multi-modal MRI, which extends the well-known UNet. Compared to single images, multi-modal data brings complementary information, contributing to better data representation and discriminative power. Our contributions are three-fold. First, how to effectively integrate and fully leverage multi-modal data remains almost unexplored. In this work, each MRI modality is processed in a different path to better exploit their unique information. Second, inspired by HyperDenseNet, the network is densely-connected both within each path and across different paths, granting the model the freedom to learn where and how the different modalities should be processed and combined. Third, we improved standard U-Net modules by extending inception modules with two dilated convolutions blocks of different scale, which helps handling multi-scale context. We report experiments over the data set of the public MICCAI 2018 Challenge on Automatic Intervertebral Disc Localization and Segmentation, with 13 multi-modal MRI images used for training and 3 for validation. We trained IVD-Net on an NVidia TITAN XP GPU with 16 GBs RAM, using ADAM as optimizer and a learning rate of 10e-5 during 200 epochs. Training took about 5 hours, and segmentation of a whole volume about 2-3 seconds, on average. Several baselines, with different multi-modal fusion strategies, were used to demonstrate the effectiveness of the proposed architecture.

Includes members:
  Member "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities":
    also connected through other hyperedges:
      (none)
  Member "multi modal convolutional neural networks for brain tumor segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dense multi path u net for ischemic stroke lesion segmentation in multiple image modalities")
  Member "hyperdense net a hyper densely connected cnn for multi modal image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "shufflenet an extremely efficient convolutional neural network for mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "isointense infant brain segmentation with a hyper dense connected convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hyperdense net a hyper densely connected cnn for multi modal image segmentation")
  Member "deep cnn ensembles and suggestive annotations for infant brain mri segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiregion segmentation of bladder cancer structures in mri with progressive dilated convolutional 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)