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 with Geometric Occlusion Estimation and Fusion of Multiple Frames"
  Abstract: Optical flow research has made significant progress in recent years and it can now be computed efficiently and accurately for many images. However, complex motions, large displacements, and difficult imaging conditions are still problematic. In this paper, we present a framework for estimating optical flow which leads to improvements on these difficult cases by 1) estimating occlusions and 2) using additional temporal information. First, we divide the image into discrete triangles and show how this allows for occluded regions to be naturally estimated and directly incorporated into the optimization algorithm. We additionally propose a novel method of dealing with temporal information in image sequences by using “inertial estimates” of the flow. These estimates are combined using a classifier-based fusion scheme, which significantly improves results. These contributions are evaluated on three different optical flow datasets, and we achieve state-of-the-art results on MPI-Sintel.

Connected through hyperedge HE-1 (induced by source paper "Are we ready for autonomous driving? The KITTI vision benchmark suite"):
  with members:
    - "End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars"
    - "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"
    - "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow"
    - "WildDash - Creating Hazard-Aware Benchmarks"
    - "Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement"
    - "Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data."
    - "Developing Vision-based and Cooperative Vehicular Embedded Systems for Enhancing Road Monitoring Services"
    - "A Flexible High-Resolution Real-Time Low-Power Stereo Vision Engine"

Connected through hyperedge HE-2 (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"
    - "Optical Flow Estimation with Channel Constancy"
    - "Coarse-to-Fine Minimization of Some Common Nonconvexities"

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