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: "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
  Abstract: Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.

Connected through hyperedge HE-1 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a survey on deep transfer learning"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "deep contextualized word representations"
    - "video salient object detection via fully convolutional networks"
    - "adversarial learning for semi supervised semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a benchmark for breast ultrasound image segmentation busis"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "brain tumor segmentation with deep neural networks"
    - "towards ct quality ultrasound imaging using deep learning"
    - "deep learning in rf sub sampled b mode ultrasound imaging"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-3 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "brain tumor segmentation with deep neural networks"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "dual path networks"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"

Connected through hyperedge HE-4 (induced by source paper "tomogan low dose synchrotron x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "tomogan low dose x ray tomography with generative adversarial networks"):
  with members:
    - "a deep journey into super resolution a survey"
    - "denoising imaging polarimetry by adapted bm3d method"
    - "dlhub model and data serving for science"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep convolutional neural network for inverse problems in imaging"
    - "improved training of wasserstein gans"
    - "lose the views limited angle ct reconstruction via implicit sinogram completion"

Connected through hyperedge HE-6 (induced by source paper "2d and 3d segmentation of uncertain local collagen fiber orientations in shg microscopy"):
  with members:
    - "focal loss for dense object detection"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "mind2mind transfer learning for gans"):
  with members:
    - "deep learning for classical japanese literature"
    - "pros and cons of gan evaluation measures"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "a survey on deep transfer learning"
    - "transferring gans generating images from limited data"
    - "progressive growing of gans for improved quality stability and variation"
    - "adversarially regularized autoencoders"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"):
  with members:
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "image reconstruction by domain transform manifold learning"
    - "improved training of wasserstein gans"
    - "u net convolutional networks for biomedical image segmentation"
    - "on the difficulty of training recurrent neural networks"

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)