human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Linear dimensionality reduction based on Hybrid structure preserving projections"
  Abstract: Recent advances have shown the methods based on local structure preserving projections can effectively learn discriminative features. The two attractive approaches for characterizing such data structure are: the classical nearest neighbor strategy for neighborhood structure and the sparse coding algorithm for sparsity structure. Motivated by the intuitive analysis of the relationship between the two structures, in this paper, we take both of them into account and propose two integrated approaches for dimensionality reduction. Concretely, we for achieving improvement directly integrate two available objectives, utilizing neighborhood structure and based on sparsity structure, to construct the combined method, briefly called CSNP. However, such rough strategy often results in its degradation in practice. Instead of the superficial combination, we exploit a hybrid structure by intergrading the two structures and then propose the Sparsity and Neighborhood Preserving Projections, dubbed SNPP, by preserving the hybrid structure into reduced subspace. The resulting optimization problems can be also interpreted as an instance of the general graph embedding framework and can reduce to the generalized eigenvalue decomposition problem. Finally, we conduct extensive experiments on publicly available data sets to verify the efficacy of our algorithms. From the experimental results, we roughly draw the conclusion that neighborhood structure is more important for low-dimensional data while sparsity structure is more useful for high-dimensional data. Construct a hybrid representation to characterize data structure.Perform dimensionality reduction by preserving a hybrid structure.Explicitly discuss the sparsity and the neighborhood depending on datasets.Take multiple structures into account for better algorithm performance.

Connected through hyperedge HE-1 (induced by source paper "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries"):
  with members:
    - "Sparse Representation for Computer Vision and Pattern Recognition"
    - "Quasi Real-Time Summarization for Consumer Videos"
    - "Is Denoising Dead"
    - "Fields of Experts: a framework for learning image priors"
    - "A Probabilistic Framework for Discriminative Dictionary Learning"
    - "Image Denoising in Mixed Poisson–Gaussian Noise"
    - "Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization"
    - "Image Denoising and Inpainting with Deep Neural Networks"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Mathematics