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: "Spam Filtering through Anomaly Detection"
  Abstract: More than 85% of received e-mails are spam. Spam is an important issue for computer security because it is used to spread other threats such as computer viruses, worms or phishing. Classic techniques to fight spam, including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Machine-learning techniques trained using statistical representations of the terms that usually appear in the e-mails are widely used in the literature. However, these methods demand a time-consuming training step with labelled data. Dealing with the situation where the availability of labelled training instances is limited slows down the progress of filtering systems and offers advantages to spammers. In this paper, we present the first spam filtering method based on anomaly detection that reduces the necessity of labelling spam messages and only uses the representation of legitimate e-mails. This approach represents legitimate e-mails as word frequency vectors. Thereby, an email is classified as spam or legitimate by measuring its deviation to the representation of these legitimate e-mails. This method achieves high accuracy rates detecting spam and maintains a low false positive rate, reducing the effort produced by labelling spam.

Connected through hyperedge HE-1 (induced by source paper "Introduction to the special issue on word sense disambiguation: the state of the art"):
  with members:
    - "Learning rules for large vocabulary word sense disambiguation"
    - "Naive Bayes and Exemplar-based approaches to Word Sense Disambiguation revisited"
    - "Massively Parallel Parsing: A Strongly Interactive Model of Natural Language Interpretation*"
    - "A graph-based approach to WSD using relevant semantic trees and n-cliques model"
    - "Framework and Results for French"

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