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: "insights into analysis operator learning from patch based sparse models to higher order mrfs"
  Abstract: This paper addresses a new learning algorithm for the recently introduced co-sparse analysis model. First, we give new insights into the co-sparse analysis model by establishing connections to filter-based MRF models, such as the field of experts model of Roth and Black. For training, we introduce a technique called bi-level optimization to learn the analysis operators. Compared with existing analysis operator learning approaches, our training procedure has the advantage that it is unconstrained with respect to the analysis operator. We investigate the effect of different aspects of the co-sparse analysis model and show that the sparsity promoting function (also called penalty function) is the most important factor in the model. In order to demonstrate the effectiveness of our training approach, we apply our trained models to various classical image restoration problems. Numerical experiments show that our trained models clearly outperform existing analysis operator learning approaches and are on par with state-of-the-art image denoising algorithms. Our approach develops a framework that is intuitive to understand and easy to implement.

Connected through hyperedge HE-1 (induced by source paper "controlling neural networks via energy dissipation"):
  with members:
    - "linear inequality constraints for neural network activations"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "homogeneous linear inequality constraints for neural network activations"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "scale recurrent network for deep image deblurring"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "deep image prior"
    - "recurrent relational networks"

Connected through hyperedge HE-2 (induced by source paper "truncated back propagation for bilevel optimization"):
  with members:
    - "introduction to online convex optimization"
    - "learning to optimize neural nets"
    - "online learning rate adaptation with hypergradient descent"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to learn by gradient descent by gradient descent"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"

Connected through hyperedge HE-3 (induced by source paper "learning generic diffusion processes for image restoration"):
  with members:
    - "learning deep cnn denoiser prior for image restoration"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "deep residual learning for image recognition"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "universal denoising networks a novel cnn architecture for image denoising"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "non local color image denoising with convolutional neural networks"
    - "deep gaussian conditional random field network a model based deep network for discriminative denoising"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "accelerating gmm based patch priors for image restoration three ingredients for a 100 times speed up"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "adaptive image denoising by mixture adaptation"
    - "example based image synthesis via randomized patch matching"
    - "accurate image super resolution using very deep convolutional networks"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "image super resolution using deep convolutional networks"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"

Connected through hyperedge HE-6 (induced by source paper "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "sparsity based poisson denoising with dictionary learning"
    - "learning activation functions to improve deep neural networks"
    - "revisiting loss specific training of filter based mrfs for image restoration"

Connected through hyperedge HE-7 (induced by source paper "learning co sparse analysis operators with separable structures"):
  with members:
    - "sparse and spurious dictionary learning with noise and outliers"
    - "learning ell_1 based analysis and synthesis sparsity priors using bi level optimization"
    - "separable cosparse analysis operator learning"
    - "a bimodal co sparse analysis model for image processing"
    - "separable dictionary learning"
    - "analysis based blind compressive sensing"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-8 (induced by source paper "support driven wavelet frame based image deblurring"):
  with members:
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "group based sparse representation for image restoration"
    - "sparsity based methods for overparameterized variational problems"
    - "sparse signal reconstruction via iterative support detection"

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)