human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "A Probabilistic Framework for Discriminative Dictionary Learning"):
  Abstract: In this paper, we address the problem of discriminative dictionary learning (DDL), where sparse linear representation and classification are combined in a probabilistic framework. As such, a single discriminative dictionary and linear binary classifiers are learned jointly. By encoding sparse representation and discriminative classification models in a MAP setting, we propose a general optimization framework that allows for a data-driven tradeoff between faithful representation and accurate classification. As opposed to previous work, our learning methodology is capable of incorporating a diverse family of classification cost functions (including those used in popular boosting methods), while avoiding the need for involved optimization techniques. We show that DDL can be solved by a sequence of updates that make use of well-known and well-studied sparse coding and dictionary learning algorithms from the literature. To validate our DDL framework, we apply it to digit classification and face recognition and test it on standard benchmarks.

Includes members:
  Member "Sparse Representation for Computer Vision and Pattern Recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Multiple Kernel Learning for Hyperspectral Image Classification: A Review")
  Member "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "An Integrated Model for Bayesian Learning of Sparse Representation and Classifier Training")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science