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: "Batch gradient method with smoothing L1/ 2 regularization for training of feedforward neural networks"
  Abstract: The aim of this paper is to develop a novel method to prune feedforward neural networks by introducing an L"1"/"2 regularization term into the error function. This procedure forces weights to become smaller during the training and can eventually removed after the training. The usual L"1"/"2 regularization term involves absolute values and is not differentiable at the origin, which typically causes oscillation of the gradient of the error function during the training. A key point of this paper is to modify the usual L"1"/"2 regularization term by smoothing it at the origin. This approach offers the following three advantages: First, it removes the oscillation of the gradient value. Secondly, it gives better pruning, namely the final weights to be removed are smaller than those produced through the usual L"1"/"2 regularization. Thirdly, it makes it possible to prove the convergence of the training. Supporting numerical examples are also provided.

Connected through hyperedge HE-1 (induced by source paper "Boundedness and Convergence of Online Gradient Method With Penalty for Feedforward Neural Networks"):
  with members:
    - "Regularization Networks and Support Vector Machines"
    - "Simplifying neural networks by soft weight-sharing"

Connected through hyperedge HE-2 (induced by source paper "Modeling of complex dynamic systems using differential neural networks with the incorporation of a priori knowledge"):
  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