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: "k space deep learning for accelerated mri"
  Abstract: The annihilating filter-based low-rank Hanel matrix approach (ALOHA) is one of the state-of-the-art compressed sensing approaches that directly interpolates the missing k-space data using low-rank Hankel matrix completion. Inspired by the recent mathematical discovery that links deep neural networks to Hankel matrix decomposition using data-driven framelet basis, here we propose a fully data-driven deep learning algorithm for k-space interpolation. Our network can be also easily applied to non-Cartesian k-space trajectories by simply adding an additional re-gridding layer. Extensive numerical experiments show that the proposed deep learning method significantly outperforms the existing image-domain deep learning approaches.

Connected through hyperedge HE-1 (induced by source paper "understanding geometry of encoder decoder cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a differential topological view of challenges in learning with feedforward neural networks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "optimization landscape and expressivity of deep cnns"

Connected through hyperedge HE-2 (induced by source paper "reducing uncertainty in undersampled mri reconstruction with active acquisition"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "image reconstruction by domain transform manifold learning"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "energy based generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "deep compressed sensing"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "boosting cnn beyond label in inverse problems"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "spectral normalization for generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "training deep learning based denoisers without ground truth data"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "riemannian approach to batch normalization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "super resolution mri using finite rate of innovation curves"

Connected through hyperedge HE-6 (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"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "u net convolutional networks for biomedical image segmentation"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "non learning based deep parallel mri reconstruction nldpmri"):
  with members:
    - "deep energy using energy functions for unsupervised training of dnns"
    - "deep energy unsupervised training of deep neural networks"
    - "image reconstruction by domain transform manifold learning"
    - "deep image prior"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "k space deep learning for reference free epi ghost correction"
    - "dimension dynamic mr imaging with both k space and spatial prior knowledge obtained via multi supervised network training"
    - "deep image prior"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "u net convolutional networks for biomedical image segmentation"

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)