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 "synseg net synthetic segmentation without target modality ground truth"):
  Abstract: A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels. SynSeg-Net is trained by using (1) unpaired intensity images from source and target modalities, and (2) manual labels only from source modality. SynSeg-Net is enabled by the recent advances of cycle generative adversarial networks (CycleGAN) and DCNN. We evaluate the performance of the SynSeg-Net on two experiments: (1) MRI to CT splenomegaly synthetic segmentation for abdominal images, and (2) CT to MRI total intracranial volume synthetic segmentation (TICV) for brain images. The proposed end-to-end approach achieved superior performance to two stage methods. Moreover, the SynSeg-Net achieved comparable performance to the traditional segmentation network using target modality labels in certain scenarios. The source code of SynSeg-Net is publicly available (this https URL).

Includes members:
  Member "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "distribution matching losses can hallucinate features in medical image translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visualisation of medical image fusion and translation for accurate diagnosis of high grade gliomas")
  Member "segan adversarial network with multi scale l 1 loss for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "towards adversarial retinal image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review")
  Member "gans for biological image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "i bet you are wrong gambling adversarial networks for structured semantic 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)