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: "Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case"
  Abstract: A probabilistic model allows us to reason about the world and make statistically optimal decisions using Bayesian decision theory. However, in practice the intractability of the decision problem forces us to adopt simplistic loss functions such as the 0/1 loss or Hamming loss and as result we make poor decisions through MAP estimates or through low-order marginal statistics. In this work we investigate optimal decision making for more realistic loss functions. Specifically we consider the popular intersection-over-union (IoU) score used in image segmentation benchmarks and show that it results in a hard combinatorial decision problem. To make this problem tractable we propose a statistical approximation to the objective function, as well as an approximate algorithm based on parametric linear programming. We apply the algorithm on three benchmark datasets and obtain improved intersection-over-union scores compared to maximum-posterior-marginal decisions. Our work points out the difficulties of using realistic loss functions with probabilistic computer vision models.

Connected through hyperedge HE-1 (induced by source paper "Real-time human pose recognition in parts from single depth images"):
  with members:
    - "Multimodal Multipart Learning for Action Recognition in Depth Videos"
    - "Classifying and visualizing motion capture sequences using deep neural networks"
    - "Efficient Model-based 3D Tracking of Hand Articulations using Kinect"
    - "Histogram of 3D Facets: A characteristic descriptor for hand gesture recognition"
    - "Dynamic time warping for gesture-based user identification and authentication with Kinect"
    - "Discriminative learning of visual words for 3D human pose estimation"
    - "Using Affect Awareness to Modulate Task Experience: A Study Amongst Pre-elementary School Kids"
    - "Action recognition robust to background clutter by using stereo vision"

Connected through hyperedge HE-2 (induced by source paper "Perceptually Inspired Layout-Aware Losses for Image Segmentation"):
  with members:
    (none)

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