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: "working locally thinking globally theoretical guarantees for convolutional sparse coding"
  Abstract: The celebrated sparse representation model has led to remarkable results in various signal processing tasks in the last decade. However, despite its initial purpose of serving as a global prior for entire signals, it has been commonly used for modeling low dimensional patches due to the computational constraints it entails when deployed with learned dictionaries. A way around this problem has been recently proposed, adopting a convolutional sparse representation model. This approach assumes that the global dictionary is a concatenation of banded circulant matrices. While several works have presented algorithmic solutions to the global pursuit problem under this new model, very few truly-effective guarantees are known for the success of such methods. In this paper, we address the theoretical aspects of the convolutional sparse model providing the first meaningful answers to questions of uniqueness of solutions and success of pursuit algorithms, both greedy and convex relaxations, in ideal and noisy regimes. To this end, we generalize mathematical quantities, such as the    $\ell _0$   norm, mutual coherence, Spark and restricted isometry property to their counterparts in the convolutional setting, intrinsically capturing local measures of the global model. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.

Connected through hyperedge HE-1 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding"):
  with members:
    - "on first order meta learning algorithms"
    - "densely connected convolutional networks for speech recognition"
    - "memory efficient implementation of densenets"
    - "meta sgd learning to learn quickly for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks analysed via inverse problem theory and sparse representations"):
  with members:
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "raisr rapid and accurate image super resolution"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "learning to deblur"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning in hierarchical networks"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-4 (induced by source paper "neural rendering model joint generation and prediction for semi supervised learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exploring generalization in deep learning"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mathematics of deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-5 (induced by source paper "a bayesian perspective of convolutional neural networks through a deconvolutional generative model"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "exploring generalization in deep learning"
    - "semi supervised learning with gans manifold invariance with improved inference"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mathematics of deep learning"
    - "emergence of invariance and disentanglement in deep representations"
    - "squeeze and excitation networks"
    - "spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-6 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-7 (induced by source paper "on multi layer basis pursuit efficient algorithms and convolutional neural networks"):
  with members:
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "deep component analysis via alternating direction neural networks"
    - "trainlets dictionary learning in high dimensions"
    - "densely connected convolutional networks"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "sparse recovery with coherent tight frames via analysis dantzig selector and analysis lasso"

Connected through hyperedge HE-8 (induced by source paper "a local block coordinate descent algorithm for the convolutional sparse coding model"):
  with members:
    - "multi focus image fusion via coupled sparse representation and dictionary learning"
    - "scalable online convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "an overview of gradient descent optimization algorithms"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

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.IT(Information Theory)