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: "super resolution mri using finite rate of innovation curves"
  Abstract: We propose a two-stage algorithm for the super-resolution of MR images from their low-frequency k-space samples. In the first stage we estimate a resolution-independent mask whose zeros represent the edges of the image. This builds off recent work extending the theory of sampling signals of finite rate of innovation (FRI) to two-dimensional curves. We enable its application to MRI by proposing extensions of the signal models allowed by FRI theory, and by developing a more robust and efficient means to determine the edge mask. In the second stage of the scheme, we recover the super-resolved MR image using the discretized edge mask as an image prior. We evaluate our scheme on simulated single-coil MR data obtained from analytical phantoms, and compare against total variation reconstructions. Our experiments show improved performance in both noiseless and noisy settings.

Connected through hyperedge HE-1 (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-2 (induced by source paper "deep generalization of structured low rank algorithms deep slr"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "structured low rank algorithms theory mr applications and links to machine learning"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "off the grid model based deep learning o modl"
    - "k space deep learning for accelerated mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "a fast algorithm for convolutional structured low rank matrix recovery"

Connected through hyperedge HE-3 (induced by source paper "calibration free b0 correction of epi data using structured low rank matrix recovery"):
  with members:
    - "recovery of damped exponentials using structured low rank matrix completion"
    - "navigator free epi ghost correction with structured low rank matrix models new theory and methods"
    - "novel structured low rank algorithm to recover spatially smooth exponential image time series"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-4 (induced by source paper "a fast algorithm for convolutional structured low rank matrix recovery"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "structured low rank recovery of piecewise constant signals with performance guarantees"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "recovery of piecewise smooth images from few fourier samples"
    - "off the grid recovery of piecewise constant images from few fourier samples"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-5 (induced by source paper "structured low rank recovery of piecewise constant signals with performance guarantees"):
  with members:
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "recovery of piecewise smooth images from few fourier samples"
    - "off the grid recovery of piecewise constant images from few fourier samples"
    - "robust spectral compressed sensing via structured matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-6 (induced by source paper "recovery of piecewise smooth images from few fourier samples"):
  with members:
    - "atomic norm denoising with applications to line spectral estimation"
    - "super resolution from noisy data"

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)