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: "understanding geometry of encoder decoder cnns"
  Abstract: Encoder-decoder networks using convolutional neural network (CNN) architecture have been extensively used in deep learning literatures thanks to its excellent performance for various inverse problems in computer vision, medical imaging, etc. However, it is still difficult to obtain coherent geometric view why such an architecture gives the desired performance. Inspired by recent theoretical understanding on generalizability, expressivity and optimization landscape of neural networks, as well as the theory of convolutional framelets, here we provide a unified theoretical framework that leads to a better understanding of geometry of encoder-decoder CNNs. Our unified mathematical framework shows that encoder-decoder CNN architecture is closely related to nonlinear basis representation using combinatorial convolution frames, whose expressibility increases exponentially with the network depth. We also demonstrate the importance of skipped connection in terms of expressibility, and optimization landscape.

Connected through hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction"):
  with members:
    - "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"
    - "deep generative adversarial networks for compressed sensing automates mri"

Connected through hyperedge HE-2 (induced by source paper "understanding graph isomorphism network for brain mr functional connectivity analysis"):
  with members:
    - "graph neural network for interpreting task fmri biomarkers"
    - "a comprehensive survey on graph neural networks"
    - "similarity learning with higher order graph convolutions for brain network analysis"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "graph saliency maps through spectral convolutional networks application to sex classification with brain connectivity"
    - "similarity learning with higher order proximity for brain network analysis"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-3 (induced by source paper "boosting cnn beyond label in inverse problems"):
  with members:
    - "noise2void learning denoising from single noisy images"
    - "noise2noise learning image restoration without clean data"
    - "spectral normalization for generative adversarial networks"
    - "k space deep learning for accelerated mri"
    - "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-4 (induced by source paper "deep learning diffuse optical tomography"):
  with members:
    - "efficient and accurate inversion of multiple scattering with deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"
    - "deep learning for photoacoustic tomography from sparse data"
    - "deep convolutional neural network for inverse problems in imaging"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "structured low rank algorithms theory mr applications and links to machine learning"):
  with members:
    - "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"
    - "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-6 (induced by source paper "k space deep learning for accelerated mri"):
  with members:
    - "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"
    - "compressive sampling using annihilating filter based low rank interpolation"

Connected through hyperedge HE-7 (induced by source paper "k space deep learning for reference free epi ghost correction"):
  with members:
    - "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"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "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 "one network to solve all rois deep learning ct for any roi using differentiated backprojection"):
  with members:
    - "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 convolutional neural network for inverse problems in imaging"
    - "deep residual learning for compressed sensing ct reconstruction via persistent homology analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "matconvnet convolutional neural networks for matlab"

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.LG(Machine Learning)