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: "plug and play methods provably converge with properly trained denoisers"
  Abstract: Plug-and-play (PnP) is a non-convex framework that integrates modern denoising priors, such as BM3D or deep learning-based denoisers, into ADMM or other proximal algorithms. An advantage of PnP is that one can use pre-trained denoisers when there is not sufficient data for end-to-end training. Although PnP has been recently studied extensively with great empirical success, theoretical analysis addressing even the most basic question of convergence has been insufficient. In this paper, we theoretically establish convergence of PnP-FBS and PnP-ADMM, without using diminishing stepsizes, under a certain Lipschitz condition on the denoisers. We then propose real spectral normalization, a technique for training deep learning-based denoisers to satisfy the proposed Lipschitz condition. Finally, we present experimental results validating the theory.

Connected through hyperedge HE-1 (induced by source paper "deep plug and play prior for low rank tensor completion"):
  with members:
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "low rank tensor completion for multiway visual data"
    - "regularization by denoising clarifications and new interpretations"
    - "non local meets global an integrated paradigm for hyperspectral denoising"
    - "tensor n tubal rank and its convex relaxation for low rank tensor recovery"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "image restoration by iterative denoising and backward projections"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-2 (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"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "regularization by denoising clarifications and new interpretations"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "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-3 (induced by source paper "learning to optimize in swarms"):
  with members:
    - "abd net attentive but diverse person re identification"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "learned optimizers that scale and generalize"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to optimize"
    - "neural architecture search with reinforcement learning"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "accelerating optimization algorithms with dynamic parameter selections using convolutional neural networks for inverse problems in image processing"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "learning to optimize"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "u net convolutional networks for biomedical image segmentation"
    - "message passing algorithms for compressed sensing"

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)