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: "Activity recognition with weighted frequent patterns mining in smart environments"
  Abstract: We propose an efficient frequent activity patterns mining in smart environments.We build an accurate activity classifier based on the mined frequent patterns.We distinguish overlapped activities with global and local weights of sensor events.We use publicly available dataset of smart environments to validate our methods. In the past decades, activity recognition has aroused a great interest for the research groups majoring in context-awareness computing and human behaviours monitoring. However, the correlations between the activities and their frequent patterns have never been directly addressed by traditional activity recognition techniques. As a result, activities that trigger the same set of sensors are difficult to differentiate, even though they present different patterns such as different frequencies of the sensor events. In this paper, we propose an efficient association rule mining technique to find the association rules between the activities and their frequent patterns, and build an activity classifier based on these association rules. We also address the classification of overlapped activities by incorporating the global and local weight of the patterns. The experiment results using publicly available dataset demonstrate that our method is able to achieve better performance than traditional recognition methods such as Decision Tree, Naive Bayesian and HMM. Comparison studies show that the proposed association rule mining method is efficient, and we can further improve the activity recognition accuracy by considering global and local weight of frequent patterns of activities.

Connected through hyperedge HE-1 (induced by source paper "Activity Discovery and Detection of Behavioral Deviations of an Inhabitant From Binary Sensors"):
  with members:
    - "The human behaviour indicator: A measure of behavioural evolution"

Connected through hyperedge HE-2 (induced by source paper "Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach"):
  with members:
    - "Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application"

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