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: "Online pose classification and walking speed estimation using handheld devices"
  Abstract: We describe and evaluate two methods for device pose classification and walking speed estimation that generalize well to new users, compared to previous work. These machine learning based methods are designed for the general case of a person holding a mobile device in an unknown location and require only a single low-cost, low-power sensor: a triaxial accelerometer. We evaluate our methods in straight-path indoor walking experiments as well as in natural indoor walking settings. Experiments with 14 human participants to test user generalization show that our pose classifier correctly selects among four device poses with 94% accuracy compared to 82% for previous work, and our walking speed estimates are within 12-15% (straight/indoor walk) of ground truth compared to 17-22% for previous work. Implementation on a mobile phone demonstrates that both methods can run efficiently online.

Connected through hyperedge HE-1 (induced by source paper "Regularization Networks and Support Vector Machines"):
  with members:
    - "On the Noise Model of Support Vector Machines Regression"
    - "A fast iterative algorithm for fisher discriminant using heterogeneous kernels"
    - "Laplacian twin support vector machine for semi-supervised classification"
    - "Symbol design: a user-centered method to design pen-based interfaces and extend the functionality of pointer input devices"
    - "Boundedness and Convergence of Online Gradient Method With Penalty for Feedforward Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal"):
  with members:
    - "Similar gait action recognition using an inertial sensor"

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