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: "seagle sparsity driven image reconstruction under multiple scattering"
  Abstract: Multiple scattering of an electromagnetic wave as it passes through an object is a fundamental problem that limits the performance of current imaging systems. In this paper, we describe a new technique-called Series Expansion with Accelerated Gradient Descent on Lippmann-Schwinger Equation (SEAGLE)-for robust imaging under multiple scattering based on a combination of a new nonlinear forward model and a total variation (TV) regularizer. The proposed forward model can account for multiple scattering, which makes it advantageous in applications where linear models are inaccurate. Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method. This expansion guarantees the convergence even for strongly scattering objects. One of our key insights is that it is possible to obtain an explicit formula for computing the gradient of our nonlinear forward model with respect to the unknown object, thus enabling fast image reconstruction with the state-of-the-art fast iterative shrinkage/thresholding algorithm (FISTA). The proposed method is validated on both simulated and experimentally measured data.

Connected through hyperedge HE-1 (induced by source paper "an online plug and play algorithm for regularized image reconstruction"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "online convolutional dictionary learning for multimodal imaging"
    - "plug and play unplugged optimization free reconstruction using consensus equilibrium"

Connected through hyperedge HE-2 (induced by source paper "stability of scattering decoder for nonlinear diffractive imaging"):
  with members:
    - "efficient inversion of multiple scattering model for optical diffraction tomography"
    - "deep convolutional neural network for inverse problems in imaging"
    - "a recursive born approach to nonlinear inverse scattering"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "efficient inversion of multiple scattering model for optical diffraction tomography"):
  with members:
    - "a recursive born approach to nonlinear inverse scattering"
    - "compressive imaging with iterative forward models"

Connected through hyperedge HE-4 (induced by source paper "accelerated image reconstruction for nonlinear diffractive imaging"):
  with members:
    - "a recursive born approach to nonlinear inverse scattering"
    - "compressive imaging with iterative forward models"

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)