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: "a transfer learning approach for accelerated mri using deep neural networks"
  Abstract: Purpose: Neural networks have received recent interest for reconstruction of undersampled MR acquisitions. Ideally network performance should be optimized by drawing the training and testing data from the same domain. In practice, however, large datasets comprising hundreds of subjects scanned under a common protocol are rare. The goal of this study is to introduce a transfer-learning approach to address the problem of data scarcity in training deep networks for accelerated MRI. #R##N#Methods: Neural networks were trained on thousands of samples from public datasets of either natural images or brain MR images. The networks were then fine-tuned using only few tens of brain MR images in a distinct testing domain. Domain-transferred networks were compared to networks trained directly in the testing domain. Network performance was evaluated for varying acceleration factors (2-10), number of training samples (0.5-4k) and number of fine-tuning samples (0-100). #R##N#Results: The proposed approach achieves successful domain transfer between MR images acquired with different contrasts (T1- and T2-weighted images), and between natural and MR images (ImageNet and T1- or T2-weighted images). Networks obtained via transfer-learning using only tens of images in the testing domain achieve nearly identical performance to networks trained directly in the testing domain using thousands of images. #R##N#Conclusion: The proposed approach might facilitate the use of neural networks for MRI reconstruction without the need for collection of extensive imaging datasets.

Connected through hyperedge HE-1 (induced by source paper "generalising deep learning mri reconstruction across different domains"):
  with members:
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-2 (induced by source paper "fastmri an open dataset and benchmarks for accelerated mri"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "deep image prior"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "inversenet solving inverse problems with splitting networks"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "admm net a deep learning approach for compressive sensing mri"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"

Connected through hyperedge HE-3 (induced by source paper "global guarantees for enforcing deep generative priors by empirical risk"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "phase retrieval under a generative prior"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "a convergence theory for deep learning via over parameterization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

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)