human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sensemycity crowdsourcing an urban sensor"
  Abstract: People treat smartphones as a second skin, having them around nearly 24/7 and constantly interacting with them. Although smartphones are used mainly for personal communication, social networking and web browsing, they have many connectivity capabilities, and are at the same time equipped with a wide range of embedded sensors. Additionally, bluetooth connectivity can be leveraged to collect data from external sensors, greatly extending the sensing capabilities. However, massive data-gathering using smartphones still poses many architectural challenges, such as limited battery and processing power, and possibly connectivity costs. #R##N#This article describes SenseMyCity (SMC), an Internet of Things mobile urban sensor that is extensible and fully configurable. The platform consists of an app, a backoffice and a frontoffice. The SMC app can collect data from embedded sensors, like GPS, wifi, accelerometer, magnetometer, etc, as well as from external bluetooth sensors, ranging from On-Board Diagnostics gathering data from vehicles, to wearable cardiac sensors. Adding support for new internal or external sensors is straightforward due to the modular architecture. Data transmission to our servers can occur either on-demand or in real-time, while keeping costs down by only using the configured type of Internet connectivity. We discuss our experience implementing the platform and using it to make longitudinal studies with many users. Further, we present results on bandwidth utilization and energy consumption for different sensors and sampling rates. Finally, we show two use cases: mapping fuel consumption and user stress extracted from cardiac sensors.

Connected through hyperedge HE-1 (induced by source paper "smartphone based vehicle telematics a ten year anniversary"):
  with members:
    - "semmatch road semantics based accurate map matching for challenging positioning data"
    - "smartloc sensing landmarks silently for smartphone based metropolitan localization"
    - "toward cloud based vehicular networks with efficient resource management"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

Connected through hyperedge HE-2 (induced by source paper "map aided dead reckoning a study on locational privacy in insurance telematics"):
  with members:
    - "privacy preserving driving style recognition"
    - "recursive bayesian initialization of localization based on ranging and dead reckoning"
    - "elastic pathing your speed is enough to track you"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CY(Computers and Society)