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: "off the grid model based deep learning o modl"
  Abstract: We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies is the learning of non-linear annihilation relations in Fourier space. We rely on a model based framework, which allows us to use a significantly smaller deep network, compared to direct approaches that also learn how to invert the forward model. Preliminary comparisons against image domain MoDL approach demonstrates the potential of the off-the-grid formulation. The main benefit of the proposed scheme compared to structured low-rank methods is the quite significant reduction in computational complexity.

Connected through hyperedge HE-1 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"
    - "neural ordinary differential equations"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "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"

Connected through hyperedge HE-2 (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"
    - "k space deep learning for accelerated mri"
    - "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-3 (induced by source paper "calibrationless parallel mri using model based deep learning c modl"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "learning a variational network for reconstruction of accelerated mri data"

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)