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: "Boundedness and Convergence of Online Gradient Method With Penalty for Feedforward Neural Networks"
  Abstract: In this brief, we consider an online gradient method with penalty for training feedforward neural networks. Specifically, the penalty is a term proportional to the norm of the weights. Its roles in the method are to control the magnitude of the weights and to improve the generalization performance of the network. By proving that the weights are automatically bounded in the network training with penalty, we simplify the conditions that are required for convergence of online gradient method in literature. A numerical example is given to support the theoretical analysis.

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"
    - "Online pose classification and walking speed estimation using handheld devices"

Connected through hyperedge HE-2 (induced by source paper "Simplifying neural networks by soft weight-sharing"):
  with members:
    - "Variable selection with neural networks"
    - "Using neural networks to model conditional multivariate densities"

Connected through hyperedge HE-3 (induced by source paper "Batch gradient method with smoothing L1/ 2 regularization for training of feedforward neural networks"):
  with members:
    - "Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge"

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