human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Regularization Networks and Support Vector Machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "On the Noise Model of Support Vector Machines Regression")
  Member "Simplifying neural networks by soft weight-sharing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Variable selection with neural networks")
  Member "Batch gradient method with smoothing L1/ 2 regularization for training of feedforward neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science