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: "Rao-Blackwellized Stochastic Gradients for Discrete Distributions."
  Abstract: We wish to compute the gradient of an expectation over a finite or countably infinite sample space having $K \leq \infty$ categories. When $K$ is indeed infinite, or finite but very large, the relevant summation is intractable. Accordingly, various stochastic gradient estimators have been proposed. In this paper, we describe a technique that can be applied to reduce the variance of any such estimator, without changing its bias---in particular, unbiasedness is retained. We show that our technique is an instance of Rao-Blackwellization, and we demonstrate the improvement it yields in empirical studies on both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "Recurrent Models of Visual Attention"):
  with members:
    - "Segmentation as selective search for object recognition"
    - "Relational inductive biases, deep learning, and graph networks."
    - "Neural self-compressor: Collective interpretation by compressing multi-layered neural networks into non-layered networks"

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