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: "IMPROVING RESIDENTIAL ENERGY CONSUMPTION AT LARGE USING PERSUASIVE SYSTEMS"
  Abstract: The paper presents a persuasive web application that stimulates residential energy conservation. The users of the application received consumption feedback that is based on electricity meter readings which they entered over a period of 6 months and which accounted for specific household characteristics. In a large scale field study which we conducted between April and September of 2010, 6’921 participants used the application. From a research perspective, the system allowed us to experimentally assess the effects of different socio-psychological concepts with regard to different measures such as popularity, choice, and energy conservation. The large user base and the real-world setting contributed to the validity of the findings. The discussion presented is structured along a behavioural change framework we adapted from Olander’s and J. Thogersen’s motivation – ability – opportunity model. Besides presenting the quantitative results of multiple studies and providing theoretical cues to outline the mechanisms behind the behaviour, we formulate guidelines that support the development of similar applications in research and industry.

Connected through hyperedge HE-1 (induced by source paper "Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application"):
  with members:
    - "BikeNet: A mobile sensing system for cyclist experience mapping"
    - "People-centric urban sensing"
    - "Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach"
    - "From taxi GPS traces to social and community dynamics: A survey"
    - "An event-driven energy efficient framework for wearable health-monitoring system"
    - "SensOrchestra: Collaborative Sensing for Symbolic Location Recognition"

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