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: "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.

Connected through hyperedge HE-1 (induced by source paper "On the Noise Model of Support Vector Machines Regression"):
  with members:
    - "A Unified Loss Function in Bayesian Framework for Support Vector Regression"

Connected through hyperedge HE-2 (induced by source paper "Boundedness and Convergence of Online Gradient Method With Penalty for Feedforward Neural Networks"):
  with members:
    - "Simplifying neural networks by soft weight-sharing"
    - "Batch gradient method with smoothing L1/ 2 regularization for training of feedforward neural networks"

Connected through hyperedge HE-3 (induced by source paper "A fast iterative algorithm for fisher discriminant using heterogeneous kernels"):
  with members:
    - "Fisher+Kernel criterion for discriminant analysis"

Connected through hyperedge HE-4 (induced by source paper "Online pose classification and walking speed estimation using handheld devices"):
  with members:
    - "User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal"

Connected through hyperedge HE-5 (induced by source paper "Symbol design: a user-centered method to design pen-based interfaces and extend the functionality of pointer input devices"):
  with members:
    - "Intelligent Interfaces to Empower People with Disabilities"

Connected through hyperedge HE-6 (induced by source paper "Laplacian twin support vector machine for semi-supervised classification"):
  with members:
    - "The Support Vector Regression with Adaptive Norms"

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