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 general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
  Abstract: Parallel MRI (pMRI) and compressed sensing MRI (CS-MRI) have been considered as two distinct reconstruction problems. Inspired by recent k-space interpolation methods, an annihilating filter based low-rank Hankel matrix approach (ALOHA) is proposed as a general framework for sparsity-driven k-space interpolation method which unifies pMRI and CS-MRI. Specifically, our framework is based on the fundamental duality between the transform domain sparsity in the primary space and the low-rankness of weighted Hankel matrix in the reciprocal space, which converts pMRI and CS-MRI to a k-space interpolation problem using structured matrix completion. Using theoretical results from the latest compressed sensing literatures, we showed that the required sampling rates for ALOHA may achieve the optimal rate. Experimental results with in vivo data for single/multi-coil imaging as well as dynamic imaging confirmed that the proposed method outperforms the state-of-the-art pMRI and CS-MRI.

Connected through hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "boosting cnn beyond label in inverse problems"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "k space deep learning for reference free epi ghost correction"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "understanding batch normalization"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"

Connected through hyperedge HE-2 (induced by source paper "quantitative susceptibility map reconstruction using annihilating filter based low rank hankel matrix approach"):
  with members:
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-3 (induced by source paper "fast and robust spectrally sparse signal recovery a provable non convex approach via robust low rank hankel matrix reconstruction"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "accelerated alternating projections for robust principal component analysis"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "structured low rank algorithms theory mr applications and links to machine learning"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "free breathing cardiac mri using bandlimited manifold modelling"
    - "calibration free b0 correction of epi data using structured low rank matrix recovery"
    - "a fast algorithm for convolutional structured low rank matrix recovery"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "off the grid recovery of piecewise constant images from few fourier samples"

Connected through hyperedge HE-5 (induced by source paper "deep residual learning for accelerated mri using magnitude and phase networks"):
  with members:
    - "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"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (induced by source paper "k space deep learning for accelerated mri"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "image reconstruction by domain transform manifold learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-7 (induced by source paper "k space deep learning for reference free epi ghost correction"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "navigator free epi ghost correction with structured low rank matrix models new theory and methods"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "u net convolutional networks for biomedical image segmentation"
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "k space deep learning for parallel mri application to time resolved mr angiography"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "k space deep learning for accelerated mri"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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.IT(Information Theory)