human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the global local dichotomy in sparsity modeling"):
  Abstract: The traditional sparse modeling approach, when applied to inverse problems with large data such as images, essentially assumes a sparse model for small overlapping data patches and processes these patches as if they were independent from each other. While producing state-of-the-art results, this methodology is suboptimal, as it does not attempt to model the entire global signal in any meaningful way—a nontrivial task by itself.

Includes members:
  Member "working locally thinking globally theoretical guarantees for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards understanding residual and dilated dense neural networks via convolutional sparse coding")
  Member "local sparsity and recovery of fusion frames structured signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uniform recovery from subgaussian multi sensor measurements")
  Member "working locally thinking globally part ii stability and algorithms for convolutional sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convolutional dictionary learning acceleration and convergence")
  Member "approximation vector machines for large scale online learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep image prior")
  Member "structured sparsity discrete and convex approaches":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "boosting of image denoising algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep k svd denoising")
  Member "sparse modeling for image and vision processing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "uniform recovery of fusion frame structured sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the importance of phase in complex compressive sensing")

Question: Please tell me which class the source paper 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)