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: "Optical Flow Estimation with Channel Constancy"
  Abstract: Large motions remain a challenge for current optical flow algorithms. Traditionally, large motions are addressed using multi-resolution representations like Gaussian pyramids. To deal with large displacements, many pyramid levels are needed and, if an object is small, it may be invisible at the highest levels. To address this we decompose images using a channel representation (CR) and replace the standard brightness constancy assumption with a descriptor constancy assumption. CRs can be seen as an over-segmentation of the scene into layers based on some image feature. If the appearance of a foreground object differs from the background then its descriptor will be different and they will be represented in different layers. We create a pyramid by smoothing these layers, without mixing foreground and background or losing small objects. Our method estimates more accurate flow than the baseline on the MPI-Sintel benchmark, especially for fast motions and near motion boundaries.

Connected through hyperedge HE-1 (induced by source paper "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"):
  with members:
    - "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion"
    - "Are we ready for autonomous driving? The KITTI vision benchmark suite"
    - "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow"
    - "Coarse-to-Fine Minimization of Some Common Nonconvexities"
    - "Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames"

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: Computer science