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: "a localization method for the internet of things"
  Abstract: Many localization algorithms and systems have been developed by means of wireless sensor networks for both indoor and outdoor environments. To achieve higher localization accuracy, extra hardware equipments are utilized by most of the existing localization solutions, which increase the cost and considerably limit the location-based applications. The Internet of Things (IOT) integrates many technologies, such as Internet, Zigbee, Bluetooth, infrared, WiFi, GPRS, 3G, etc., which can enable different ways to obtain the location information of various objects. Location-based service is a primary service of the IOT, while localization accuracy is a key issue. In this paper, a higher accuracy localization scheme is proposed which can effectively satisfy diverse requirements for many indoor and outdoor location services. The proposed scheme composes of two phases: (1) the partition phase, in which the target region is split into small grids; (2) the localization refinement phase, in which a higher accuracy of localization can be obtained by applying an algorithm designed in the paper. A trial system is set up to verify correctness of the proposed scheme and furthermore to illustrate its feasibility and availability. The experimental results show that the proposed scheme can improve the localization accuracy.

Connected through hyperedge HE-1 (induced by source paper "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"):
  with members:
    - "economics of internet of things iot an information market approach"
    - "target tracking via crowdsourcing a mechanism design approach"
    - "non stationary resource allocation policies for delay constrained video streaming application to video over internet of things enabled networks"
    - "from participatory sensing to mobile crowd sensing"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "optimal pricing and load sharing for energy saving in communications cooperation"

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.NI(Networking and Internet Architecture)