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: "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
  Abstract: Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.

Connected through hyperedge HE-1 (induced by source paper "trainable ista for sparse signal recovery"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "learning to decode linear codes using deep learning"
    - "a survey of sparse representation algorithms and applications"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"

Connected through hyperedge HE-2 (induced by source paper "iterative joint image demosaicking and denoising using a residual denoising network"):
  with members:
    - "on the convergence of adam and beyond"
    - "denoising prior driven deep neural network for image restoration"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "deep residual network for joint demosaicing and super resolution"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "inexact proximal gradient methods for non convex and non smooth optimization"

Connected through hyperedge HE-3 (induced by source paper "mc ista net adaptive measurement and initialization and channel attention optimization inspired neural network for compressive sensing"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "image super resolution using very deep residual channel attention networks"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "denoising based vector approximate message passing"
    - "deep residual learning for image recognition"
    - "new constructions of rip matrices with fast multiplication and fewer rows"

Connected through hyperedge HE-4 (induced by source paper "optimization algorithm inspired deep neural network structure design"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "recurrent inference machines for solving inverse problems"
    - "practical block wise neural network architecture generation"
    - "convolutional neural fabrics"
    - "maximal sparsity with deep networks"
    - "deep convolutional neural networks with merge and run mappings"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a flexible convolutional solver with application to photorealistic style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "neural color transfer between images"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "proximal alternating direction network a globally converged deep unrolling framework"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "stylebank an explicit representation for neural image style transfer"

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)