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 "Regularization Networks and Support Vector Machines"):
  Abstract: Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines. We review both formulations in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics. The emphasis is on regression: classification is treated as a special case.

Includes members:
  Member "On the Noise Model of Support Vector Machines Regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Unified Loss Function in Bayesian Framework for Support Vector Regression")
  Member "A fast iterative algorithm for fisher discriminant using heterogeneous kernels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Fisher+Kernel criterion for discriminant analysis")
  Member "Laplacian twin support vector machine for semi-supervised classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Support Vector Regression with Adaptive Norms")
  Member "Symbol design: a user-centered method to design pen-based interfaces and extend the functionality of pointer input devices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Intelligent Interfaces to Empower People with Disabilities")
  Member "Boundedness and Convergence of Online Gradient Method With Penalty for Feedforward Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Simplifying neural networks by soft weight-sharing")
  Member "Online pose classification and walking speed estimation using handheld devices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal")

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: Mathematics