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: "A framework for objective image quality measures based on intuitionistic fuzzy sets"
  Abstract: Abstract   Measuring the distance or similarity objectively between images is an essential and a challenging problem in various image processing and pattern recognition applications. As it is very difficult to find a certain measure that can be successfully applied to all kinds of images comparisons-related problems in the same time, it is appropriate to look for new approaches for measuring the similarity. Several similarity measures tested on numerical cases are developed in the literature based on intuitionistic fuzzy sets (IFSs) without evaluation on real data. This paper introduces a framework for using the similarity measures on IFSs in image processing field, specifically for image comparison. First, some existing similarity measures are discussed and highlighted their properties. Then, modeling digital images using IFSs is explained. Moreover, the paper introduces an intuitionistic fuzzy based image quality index measure. Second, for improving the perceived visual quality of these IFS-based similarity measures, construction of neighborhood-based similarity is proposed, which takes into consideration homogeneity of images. Finally, the proposed framework is verified on real world natural images under various types of image distortions. Experimental results confirm the effectiveness of the proposed framework in measuring the similarity between images.

Connected through hyperedge HE-1 (induced by source paper "Ear recognition using local binary patterns: A comparative experimental study"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "A Decomposition Framework for Image Denoising Algorithms"):
  with members:
    - "Is Denoising Dead"

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