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: "Classification of short texts by deploying topical annotations"
  Abstract: We propose a novel approach to the classification of short texts based on two factors: the use of Wikipedia-based annotators that have been recently introduced to detect the main topics present in an input text, represented via Wikipedia pages, and the design of a novel classification algorithm that measures the similarity between the input text and each output category by deploying only their annotated topics and the Wikipedia link-structure. Our approach waives the common practice of expanding the feature-space with new dimensions derived either from explicit or from latent semantic analysis. As a consequence it is simple and maintains a compact intelligible representation of the output categories. Our experiments show that it is efficient in construction and query time, accurate as state-of-the-art classifiers (see e.g. Phan et al. WWW '08), and robust with respect to concept drifts and input sources.

Connected through hyperedge HE-1 (induced by source paper "Improved text annotation with Wikipedia entities"):
  with members:
    - "Named entity disambiguation by leveraging wikipedia semantic knowledge"
    - "Extracting Knowledge from Web Search Engine Using Wikipedia"

Connected through hyperedge HE-2 (induced by source paper "Robust Disambiguation of Named Entities in Text"):
  with members:
    - "Named entity disambiguation by leveraging wikipedia semantic knowledge"
    - "An Entity-Topic Model for Entity Linking"
    - "AIDArabic A Named-Entity Disambiguation Framework for Arabic Text"

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: Computer science