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: "Link prediction via matrix factorization"
  Abstract: We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields better performance than using either type of feature exclusively. Finally, we propose a novel approach to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. Our model is optimized with stochastic gradient descent and scales to large graphs. Results on several datasets show the efficacy of our approach.

Connected through hyperedge HE-1 (induced by source paper "Network Growth and Link Prediction Through an Empirical Lens"):
  with members:
    - "WTF: the who to follow service at Twitter"
    - "A local random walk model for complex networks based on discriminative feature combinations"

Connected through hyperedge HE-2 (induced by source paper "Personalized recommendation on dynamic content using predictive bilinear models"):
  with members:
    - "Newsjunkie: providing personalized newsfeeds via analysis of information novelty"
    - "Contextual Recommendation based on Text Mining"
    - "Image-Based Recommendations on Styles and Substitutes"

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