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: "Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing"
  Abstract: This paper presents new fast algorithms to minimize total variation and more generally $l^1$-norms under a general convex constraint. Such problems are standards of image processing. The algorithms are based on a recent advance in convex optimization proposed by Yurii Nesterov. Depending on the regularity of the data fidelity term, we solve either a primal problem or a dual problem. First we show that standard first-order schemes allow one to get solutions of precision $\epsilon$ in $O(\frac{1}{\epsilon^2})$ iterations at worst. We propose a scheme that allows one to obtain a solution of precision $\epsilon$ in $O(\frac{1}{\epsilon})$ iterations for a general convex constraint. For a strongly convex constraint, we solve a dual problem with a scheme that requires $O(\frac{1}{\sqrt{\epsilon}})$ iterations to get a solution of precision $\epsilon$. Finally we perform some numerical experiments which confirm the theoretical results on various problems of image processing.

Connected through hyperedge HE-1 (induced by source paper "Convex optimization for multi-class image labeling with a novel family of total variation based regularizers"):
  with members:
    - "A Variational Framework for Region-Based Segmentation Incorporating Physical Noise Models"

Connected through hyperedge HE-2 (induced by source paper "Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization"):
  with members:
    - "A fast parametric maximum flow algorithm and applications"

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