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: "Voting Strategies for Anatomical Landmark Localization Using the Implicit Shape Model"
  Abstract: We address the problem of anatomical landmark localization using monocular camera information only. For person detection the Implicit Shape Model ISM is a well known method. Recently it was shown that the same local features that are used to detect persons, can be used to give rough estimates for anatomical landmark locations as well. Though the landmark localization accuracy of the original ISM is far away from being optimal. We show that a direct application of the ISM to the problem of landmark localization leads to poorly localized vote distributions. In this context, we propose three alternative voting strategies which include the use of a reference point, a simple observation vector filtering heuristic, and an observation vector weight learning algorithm. These strategies can be combined in order to further increase localization accuracy. An evaluation on the UMPM benchmark shows that these new voting strategies are able to generate compact and monotonically decreasing vote distributions, which are centered around the ground truth location of the landmarks. As a result, the ratio of correct votes can be increased from only 9.3% for the original ISM up to 42.1% if we combine all voting strategies.

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"
    - "Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case"
    - "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"

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