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: "People-centric urban sensing"
  Abstract: The vast majority of advances in sensor network research over the last five years have focused on the development of a series of small-scale (100s of nodes) testbeds and specialized applications (e.g., environmental monitoring, etc.) that are built on low-powered sensor devices that self-organize to form application-specific multihop wireless networks. We believe that sensor networks have reached an important crossroads in their development. The question we address in this paper is how to propel sensor networks from their small-scale application-specific network origins, into the commercial mainstream of people's every day lives; the challenge being: how do we develop large-scale general-purpose sensor networks for the general public (e.g., consumers) capable of supporting a wide variety of applications in urban settings (e.g., enterprises, hospitals, recreational areas, towns, cities, and the metropolis). We propose  MetroSense , a new people-centric paradigm for urban sensing at the edge of the Internet, at very large scale. We discuss a number of challenges, interactions and characteristics in urban sensing applications, and then present the MetroSense architecture which is based fundamentally on three design principles: network symbiosis, asymmetric design, and localized interaction. The ability of MetroSense to scale to very large areas is based on the use of an  opportunistic sensor networking  approach. Opportunistic sensor networking leverages mobility-enabled interactions and provides coordination between people-centric mobile sensors, static sensors and edge wireless access nodes in support of opportunistic sensing, opportunistic tasking, and opportunistic data collection. We discuss architectural challenges including providing sensing coverage with sparse mobile sensors, how to hand off roles and responsibilities between sensors, improving network performance and connectivity using adaptive multihop, and importantly, providing security and privacy for people-centric sensors and data.

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"
    - "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"
    - "IMPROVING RESIDENTIAL ENERGY CONSUMPTION AT LARGE USING PERSUASIVE SYSTEMS"

Connected through hyperedge HE-2 (induced by source paper "BikeNet: A mobile sensing system for cyclist experience mapping"):
  with members:
    - "Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application"
    - "SPS: A Novel Semantics-Aware Scheme for Location Privacy in People-Centric Sensing Network"

Connected through hyperedge HE-3 (induced by source paper "A wireless sensor network For structural monitoring"):
  with members:
    - "Fine-grained network time synchronization using reference broadcasts"
    - "Interference-aware fair rate control in wireless sensor networks"
    - "Underground structure monitoring with wireless sensor networks"
    - "Low-Complexity Cooperative Coding for Sensor Networks using Rateless and LDGM Codes"
    - "Full length article: Channel model and analysis for wireless underground sensor networks in soil medium"
    - "Rethinking Data Management for Storage-centric Sensor Networks."

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