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 "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.

Includes members:
  Member "Personalized recommendation on dynamic content using predictive bilinear models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Image-Based Recommendations on Styles and Substitutes")
  Member "Network Growth and Link Prediction Through an Empirical Lens":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "WTF: the who to follow service at Twitter")

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