human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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).

Connected through hyperedge HE-1 (induced by source paper "on direct distribution matching for adapting segmentation networks"):
  with members:
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation"
    - "a novel domain adaptation framework for medical image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "disentangle align and fuse for multimodal and zero shot image segmentation"):
  with members:
    - "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"
    - "theory and evaluation metrics for learning disentangled representations"
    - "transgaga geometry aware unsupervised image to image translation"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"

Connected through hyperedge HE-3 (induced by source paper "towards self similarity consistency and feature discrimination for unsupervised domain adaptation"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "learning discriminative features with multiple granularities for person re identification"
    - "deep visual domain adaptation a survey"
    - "ring loss convex feature normalization for face recognition"
    - "joint domain alignment and discriminative feature learning for unsupervised deep domain adaptation"
    - "image to image translation for domain adaptation"
    - "domain adaptation for visual applications a comprehensive survey"

Connected through hyperedge HE-4 (induced by source paper "adversarial pseudo healthy synthesis needs pathology factorization"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "unsupervised learning for cross domain medical image synthesis using deformation invariant cycle consistency networks"
    - "an adversarial learning approach to medical image synthesis for lesion removal"
    - "u net convolutional networks for biomedical image segmentation"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "an automatic deep learning approach for coronary artery calcium segmentation"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "adversarial synthesis learning enables segmentation without target modality ground truth"

Question: Please tell me which class the center node 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)