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: "on the convergence of learning based iterative methods for nonconvex inverse problems"
  Abstract: Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.

Connected through hyperedge HE-1 (induced by source paper "neural blind deconvolution using deep priors"):
  with members:
    - "simultaneous fidelity and regularization learning for image restoration"
    - "scale recurrent network for deep image deblurring"
    - "learning to deblur images with exemplars"
    - "learning a discriminative prior for blind image deblurring"
    - "online video deblurring via dynamic temporal blending network"
    - "deep image prior"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of admm with task adaption and beyond"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "regularization by denoising clarifications and new interpretations"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "density aware single image de raining using a multi stream dense network"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "toward designing convergent deep operator splitting methods for task specific nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "proximal alternating direction network a globally converged deep unrolling framework"

Connected through hyperedge HE-3 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling"):
  with members:
    - "fastderain a novel video rain streak removal method using directional gradient priors"
    - "plug and play methods provably converge with properly trained denoisers"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "regularization by denoising clarifications and new interpretations"
    - "acceleration of red via vector extrapolation"
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-4 (induced by source paper "bilevel integrative optimization for ill posed inverse problems"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "end to end learning of motion representation for video understanding"
    - "fast and accurate single image super resolution via information distillation network"
    - "adversarial regularizers in inverse problems"
    - "non local recurrent network for image restoration"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "deep back projection networks for super resolution"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "underexposed image correction via hybrid priors navigated deep propagation"):
  with members:
    - "kindling the darkness a practical low light image enhancer"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "residual dense network for image super resolution"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "deep image prior"
    - "deep bilateral learning for real time image enhancement"

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)