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: "real time dynamic mri reconstruction using stacked denoising autoencoder"
  Abstract: In this work we address the problem of real-time dynamic MRI reconstruction. There are a handful of studies on this topic; these techniques are either based on compressed sensing or employ Kalman Filtering. These techniques cannot achieve the reconstruction speed necessary for real-time reconstruction. In this work, we propose a new approach to MRI reconstruction. We learn a non-linear mapping from the unstructured aliased images to the corresponding clean images using a stacked denoising autoencoder (SDAE). The training for SDAE is slow, but the reconstruction is very fast - only requiring a few matrix vector multiplications. In this work, we have shown that using SDAE one can reconstruct the MRI frame faster than the data acquisition rate, thereby achieving real-time reconstruction. The quality of reconstruction is of the same order as a previous compressed sensing based online reconstruction technique.

Connected through hyperedge HE-1 (induced by source paper "neural proximal gradient descent for compressive imaging"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "searching for activation functions"
    - "unrolled optimization with deep priors"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "diracnets training very deep neural networks without skip connections"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "perceptual losses for real time style transfer and super resolution"
    - "maximal sparsity with deep networks"

Connected through hyperedge HE-2 (induced by source paper "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "learning to solve inverse problems using wasserstein loss"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "amortised map inference for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "deep generative adversarial networks for compressed sensing automates mri"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "amortised map inference for image super resolution"
    - "least squares generative adversarial networks"
    - "semantic image inpainting with deep generative models"
    - "semantic image inpainting with perceptual and contextual losses"
    - "generative visual manipulation on the natural image manifold"

Connected through hyperedge HE-4 (induced by source paper "motion compensated dynamic mri reconstruction with local affine optical flow estimation"):
  with members:
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"

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)