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: "Algorithms and applications for approximate nonnegative matrix factorization"
  Abstract: The development and use of low-rank approximate nonnegative matrix factorization (NMF) algorithms for feature extraction and identification in the fields of text mining and spectral data analysis are presented. The evolution and convergence properties of hybrid methods based on both sparsity and smoothness constraints for the resulting nonnegative matrix factors are discussed. The interpretability of NMF outputs in specific contexts are provided along with opportunities for future work in the modification of NMF algorithms for large-scale and time-varying data sets.

Connected through hyperedge HE-1 (induced by source paper "Fast Local Algorithms for Large Scale Nonnegative Matrix and Tensor Factorizations"):
  with members:
    - "A Fast Hierarchical Alternating Least Squares Algorithm for Orthogonal Nonnegative Matrix Factorization."

Connected through hyperedge HE-2 (induced by source paper "The Discrete Basis Problem"):
  with members:
    - "A generalized maximum entropy approach to bregman co-clustering and matrix approximation"
    - "Uncovering hidden structure through parallel problem decomposition"

Connected through hyperedge HE-3 (induced by source paper "On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization"):
  with members:
    - "A Converged Algorithm for Tikhonov Regularized Nonnegative Matrix Factorization with Automatic Regularization Parameters Determination."
    - "Sparse coding of human motion trajectories with non-negative matrix factorization"
    - "Efficient rank-one residue approximation method for graph regularized non-negative matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "A Converged Algorithm for Tikhonov Regularized Nonnegative Matrix Factorization with Automatic Regularization Parameters Determination."):
  with members:
    - "Decomposition-Based Transfer Distance Metric Learning for Image Classification"
    - "On the Convergence of Multiplicative Update Algorithms for Nonnegative Matrix Factorization"

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