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: "learning efficient structured sparse models"
  Abstract: We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal splitting method for the iterative solution of hierarchical sparse coding problems, and show an efficient feed forward architecture derived from its iteration. This architecture faithfully approximates the exact structured sparse codes with a fraction of the complexity of the standard optimization methods. We also show that by using different training objective functions, learnable sparse encoders are no longer restricted to be mere approximants of the exact sparse code for a pre-given dictionary, as in earlier formulations, but can be rather used as full-featured sparse encoders or even modelers. A simple implementation shows several orders of magnitude speedup compared to the state-of-the-art at minimal performance degradation, making the proposed framework suitable for real time and large-scale applications.

Connected through hyperedge HE-1 (induced by source paper "neumann networks for inverse problems in imaging"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "neural proximal gradient descent for compressive imaging"
    - "off the grid model based deep learning o modl"
    - "multi shot sensitivity encoded diffusion mri using model based deep learning modl mussels"
    - "neural ordinary differential equations"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of learning based iterative methods for nonconvex inverse problems"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"
    - "deep image prior"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "unrolled optimization with deep priors"
    - "admm net a deep learning approach for compressive sensing mri"
    - "inexact proximal gradient methods for non convex and non smooth optimization"
    - "learning to optimize"

Connected through hyperedge HE-3 (induced by source paper "amp inspired deep networks for sparse linear inverse problems"):
  with members:
    - "multi layer generalized linear estimation"
    - "hybrid approximate message passing"
    - "deep fully connected networks for video compressive sensing"
    - "learning and free energies for vector approximate message passing"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"

Connected through hyperedge HE-4 (induced by source paper "onsager corrected deep learning for sparse linear inverse problems"):
  with members:
    - "deep fully connected networks for video compressive sensing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "a deep learning approach to structured signal recovery"
    - "adam a method for stochastic optimization"
    - "image super resolution using deep convolutional networks"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "learning optimal nonlinearities for iterative thresholding algorithms"):
  with members:
    - "a deep learning approach to structured signal recovery"
    - "on learning optimized reaction diffusion processes for effective image restoration"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "learning activation functions to improve deep neural networks"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-6 (induced by source paper "discriminative recurrent sparse auto encoders"):
  with members:
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "advances in optimizing recurrent networks"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-7 (induced by source paper "learning efficient sparse and low rank models"):
  with members:
    - "decentralized sparsity regularized rank minimization algorithms and applications"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "robust sparse analysis regularization"
    - "robust pca via outlier pursuit"
    - "fast inference in sparse coding algorithms with applications to object recognition"
    - "collaborative sources identification in mixed signals via hierarchical sparse modeling"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "learning robust low rank representations"):
  with members:
    - "low rank data modeling via the minimum description length principle"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "real time robust principal components pursuit"
    - "fast inference in sparse coding algorithms with applications to object recognition"
    - "robust principal component analysis"

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.LG(Machine Learning)