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 part ii stability and algorithms for convolutional sparse coding"
  Abstract: The convolutional sparse model has recently gained increasing attention in the signal and image processing communities, and several methods have been proposed for solving the pursuit problem emerging from it -- in particular its convex relaxation, Basis Pursuit. In the first of this two-part work, we have provided a theoretical back-bone for this model, providing guarantees for the uniqueness of the sparsest solution and for the success of pursuit algorithms by introducing the notion of stripe sparsity and other related measures. Herein, we extend the analysis to a noisy regime, thereby considering signal perturbations and model deviations. We address questions of stability of the sparsest solutions and the success of pursuit algorithms, both greedy and convex. Classical definitions such as the RIP are generalized to the convolutional model, and existing notions such as the ERC are connected to our setting. 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 "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "convolutional sparse coding for high dynamic range imaging"
    - "optimization methods for convolutional sparse coding"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"
    - "fast stochastic alternating direction method of multipliers"

Connected through hyperedge HE-2 (induced by source paper "on the global local dichotomy in sparsity modeling"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "local sparsity and recovery of fusion frames structured signals"
    - "approximation vector machines for large scale online learning"
    - "structured sparsity discrete and convex approaches"
    - "boosting of image denoising algorithms"
    - "sparse modeling for image and vision processing"
    - "uniform recovery of fusion frame structured sparse signals"
    - "shift invariance sparse coding for audio classification"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning via local processing"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "trainlets dictionary learning in high dimensions"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-4 (induced by source paper "convolutional sparse coding with overlapping group norms"):
  with members:
    - "convolutional sparse representations with gradient penalties"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "optimization with sparsity inducing penalties"

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)