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: "Image-based rendering using image-based priors"
  Abstract: Given a set of images acquired from known viewpoints, we describe a method for synthesizing the image which would be seen from a new viewpoint. In contrast to existing techniques, which explicitly reconstruct the 3D geometry of the scene, we transform the problem to the reconstruction of colour rather than depth. This retains the benefits of geometric constraints, but projects out the ambiguities in depth estimation which occur in textureless regions. On the other hand, regularization is still needed in order to generate high-quality images. The paper's second contribution is to constrain the generated views to lie in the space of images whose texture statistics are those of the input images. This amounts to an image-based prior on the reconstruction which regularizes the solution, yielding realistic synthetic views. Examples are given of new view generation for cameras interpolated between the acquisition viewpoints - which enables synthetic steadicam stabilization of a sequence with a high level of realism.

Connected through hyperedge HE-1 (induced by source paper "Photorealistic scene reconstruction by voxel coloring"):
  with members:
    - "Plenoptic image editing"
    - "Layered probability maps: basic framework and prototype system"
    - "Three-dimensional scene flow"
    - "A probabilistic framework for space carving"
    - "Voxel carving for specular surfaces"
    - "Tangible interaction + graphical interpretation: a new approach to 3D modeling"
    - "Optimal Distribution of Computational Power in Free Viewpoint Interpolation by Depth Hypothesis Density Adaptation in Plane Sweeping"

Connected through hyperedge HE-2 (induced by source paper "Fields of Experts: a framework for learning image priors"):
  with members:
    - "Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey"
    - "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries"
    - "Context and Hierarchy in a Probabilistic Image Model"
    - "High-quality motion deblurring from a single image"

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