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: "an inner loop free solution to inverse problems using deep neural networks"
  Abstract: We propose a new method that uses deep learning techniques to accelerate the popular alternating direction method of multipliers (ADMM) solution for inverse problems. The ADMM updates consist of a proximity operator, a least squares regression that includes a big matrix inversion, and an explicit solution for updating the dual variables. Typically, inner loops are required to solve the first two sub-minimization problems due to the intractability of the prior and the matrix inversion. To avoid such drawbacks or limitations, we propose an inner-loop free update rule with two pre-trained deep convolutional architectures. More specifically, we learn a conditional denoising auto-encoder which imposes an implicit data-dependent prior/regularization on ground-truth in the first sub-minimization problem. This design follows an empirical Bayesian strategy, leading to so-called amortized inference. For matrix inversion in the second sub-problem, we learn a convolutional neural network to approximate the matrix inversion, i.e., the inverse mapping is learned by feeding the input through the learned forward network. Note that training this neural network does not require ground-truth or measurements, i.e., it is data-independent. Extensive experiments on both synthetic data and real datasets demonstrate the efficiency and accuracy of the proposed method compared with the conventional ADMM solution using inner loops for solving inverse problems.

Connected through hyperedge HE-1 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems"):
  with members:
    - "a unified 2d 3d large scale software environment for nonlinear inverse problems"
    - "adaptive relaxed admm convergence theory and practical implementation"
    - "image restoration using autoencoding priors"
    - "adaptive consensus admm for distributed optimization"
    - "devito automated fast finite difference computation"
    - "adaptive admm with spectral penalty parameter selection"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "jinv a flexible julia package for pde parameter estimation"

Connected through hyperedge HE-2 (induced by source paper "fast compressive sensing recovery using generative models with structured latent variables"):
  with members:
    - "task aware compressed sensing with generative adversarial networks"
    - "structured generative adversarial networks"
    - "triple generative adversarial nets"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "gan based projector for faster recovery in compressed sensing with convergence guarantees"):
  with members:
    - "a gpu oriented algorithm design for secant based dimensionality reduction"
    - "an unsupervised approach to solving inverse problems using generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "nonlocal low rank tensor factor analysis for image restoration"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "block wise lensless compressive camera"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "generalized alternating projection based total variation minimization for compressive sensing"
    - "a deep learning approach to structured signal recovery"
    - "lensless compressive imaging"
    - "compressive hyperspectral imaging with side information"

Connected through hyperedge HE-5 (induced by source paper "inversenet solving inverse problems with splitting networks"):
  with members:
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "deep convolutional neural network for inverse problems in imaging"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "photographic image synthesis with cascaded refinement networks"
    - "learning representations for automatic colorization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"
    - "improved techniques for training gans"

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)