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 interpretable optimization inspired deep network for image compressive sensing"
  Abstract: With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based methods and the speed of recent network-based ones. Specifically, we propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $\ell_1$ norm CS reconstruction model. To cast ISTA into deep network form, we develop an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms. All the parameters in ISTA-Net (\eg nonlinear transforms, shrinkage thresholds, step sizes, etc.) are learned end-to-end, rather than being hand-crafted. Moreover, considering that the residuals of natural images are more compressible, an enhanced version of ISTA-Net in the residual domain, dubbed {ISTA-Net}$^+$, is derived to further improve CS reconstruction. Extensive CS experiments demonstrate that the proposed ISTA-Nets outperform existing state-of-the-art optimization-based and network-based CS methods by large margins, while maintaining fast computational speed. Our source codes are available: \textsl{http://jianzhang.tech/projects/ISTA-Net}.

Connected through hyperedge HE-1 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent"):
  with members:
    - "chebyshev inertial landweber algorithm for linear inverse problems"
    - "degrees of freedom analysis of unrolled neural networks"
    - "trainable ista for sparse signal recovery"
    - "deep learning aided trainable projected gradient decoding for ldpc codes"
    - "neural proximal gradient descent for compressive imaging"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach"
    - "a model driven deep learning network for mimo detection"

Connected through hyperedge HE-2 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems"):
  with members:
    - "differentiable linearized admm"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "learning deep cnn denoiser prior for image restoration"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models"):
  with members:
    - "deep network classification by scattering and homotopy dictionary learning"
    - "deep energy using energy functions for unsupervised training of dnns"
    - "deep energy unsupervised training of deep neural networks"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "learning to learn by gradient descent by gradient descent"
    - "the little engine that could regularization by denoising red"
    - "maximal sparsity with deep networks"

Connected through hyperedge HE-4 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"

Connected through hyperedge HE-5 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "training image estimators without image ground truth"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "noise2noise learning image restoration without clean data"
    - "an unsupervised approach to solving inverse problems using generative adversarial networks"
    - "training deep learning based image denoisers from undersampled measurements without ground truth and without image prior"
    - "deep semantic face deblurring"
    - "unsupervised learning of depth and ego motion from video"
    - "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-7 (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"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "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-8 (induced by source paper "ifr net iterative feature refinement network for compressed sensing mri"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "deep convolutional neural network for inverse problems in imaging"
    - "convolutional recurrent neural networks for dynamic mr image reconstruction"
    - "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.CV(Computer Vision and Pattern Recognition)