human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "accelerating optimization algorithms with dynamic parameter selections using convolutional neural networks for inverse problems in image processing"):
  Abstract: Recent advances using deep neural networks (DNNs) for solving inverse problems in image processing have significantly outperformed conventional optimization algorithm based methods. Most works train DNNs to learn 1) forward models and image priors implicitly for direct mappings from given measurements to solutions, 2) data-driven priors as proximal operators in conventional iterative algorithms, or 3) forward models, priors and/or static stepsizes in unfolded structures of optimization iterations. Here we investigate another way of utilizing convolutional neural network (CNN) for empirically accelerating conventional optimization for solving inverse problems in image processing. We propose a CNN to yield parameters in optimization algorithms that have been chosen heuristically, but have shown to be crucial for good empirical performance. Our CNN-incorporated scaled gradient projection methods, without compromising theoretical properties, significantly improve empirical convergence rate over conventional optimization based methods in large-scale inverse problems such as image inpainting, compressive image recovery with partial Fourier samples, deblurring and sparse view CT. During testing, our proposed methods dynamically select parameters every iterations to speed up convergence robustly for different degradation levels, noise, or regularization parameters as compared to direct mapping approach.

Includes members:
  Member "plug and play methods provably converge with properly trained denoisers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep plug and play prior for low rank tensor completion")
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "cnn based projected gradient descent for consistent ct image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "ista net interpretable optimization inspired deep network for image compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "learning to optimize":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "tradeoffs between convergence speed and reconstruction accuracy in inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

Question: Please tell me which class the source paper 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)