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: "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.

Connected through hyperedge HE-1 (induced by source paper "convolutional recurrent neural networks for dynamic mr image reconstruction"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "deep artifact learning for compressed sensing and parallel mri"
    - "deep learning with domain adaptation for accelerated projection reconstruction mr"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "learning deep cnn denoiser prior for image restoration"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-2 (induced by source paper "working locally thinking globally theoretical guarantees for convolutional sparse coding"):
  with members:
    - "convolutional dictionary learning through tensor factorization"
    - "boosting of image denoising algorithms"
    - "optimization methods for convolutional sparse coding"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-3 (induced by source paper "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"):
  with members:
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "convolutional dictionary learning through tensor factorization"
    - "boosting of image denoising algorithms"
    - "optimization methods for convolutional sparse coding"

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)