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 deep cascade of convolutional neural networks for mr image reconstruction"
  Abstract: Inspired by recent advances in deep learning, we propose a framework for reconstructing dynamic sequences of 2D cardiac magnetic resonance (MR) images from undersampled data using a deep cascade of convolutional neural networks (CNNs) to accelerate the data acquisition process. In particular, we address the case where data is acquired using aggressive Cartesian undersampling. Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed. Secondly, when reconstructing the frames of the sequences jointly, we demonstrate that CNNs can learn spatio-temporal correlations efficiently by combining convolution and data sharing approaches. We show that the proposed method consistently outperforms state-of-the-art methods and is capable of preserving anatomical structure more faithfully up to 11-fold undersampling. Moreover, reconstruction is very fast: each complete dynamic sequence can be reconstructed in less than 10s and, for the 2D case, each image frame can be reconstructed in 23ms, enabling real-time applications.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "on instabilities of deep learning in image reconstruction does ai come at a cost"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"
    - "adversarial attacks and defences a survey"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "optimal approximation of continuous functions by very deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "modl model based deep learning architecture for inverse problems"):
  with members:
    - "model based free breathing cardiac mri reconstruction using deep learned storm priors modl storm"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unrolled optimization with deep priors"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "recurrent inference machines for solving inverse problems"

Connected through hyperedge HE-4 (induced by source paper "invert to learn to invert"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "invertible residual networks"
    - "i revnet deep invertible networks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "the reversible residual network backpropagation without storing activations"
    - "recurrent inference machines for solving inverse problems"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "density estimation using real nvp"

Connected through hyperedge HE-5 (induced by source paper "degrees of freedom analysis of unrolled neural networks"):
  with members:
    - "training deep learning based denoisers without ground truth data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled optimization with deep priors"
    - "recurrent inference machines for solving inverse problems"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "raisr rapid and accurate image super resolution"
    - "super resolution with deep convolutional sufficient statistics"

Connected through hyperedge HE-6 (induced by source paper "comparing pattern sensitivity of a convolutional neural network with an ideal observer and support vector machine"):
  with members:
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "progressive growing of gans for improved quality stability and variation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "network in network"

Connected through hyperedge HE-7 (induced by source paper "segan structure enhanced generative adversarial network for compressed sensing mri reconstruction"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "admm net a deep learning approach for compressive sensing mri"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "brain segmentation from k space with end to end recurrent attention network"):
  with members:
    - "mri reconstruction via cascaded channel wise attention network"
    - "tell me where to look guided attention inference network"
    - "joint cs mri reconstruction and segmentation with a unified deep network"
    - "u net convolutional networks for biomedical image segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)