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: "centurion incentivizing multi requester mobile crowd sensing"
  Abstract: The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

Connected through hyperedge HE-1 (induced by source paper "multimedia crowdsourcing with bounded rationality a cognitive hierarchy perspective"):
  with members:
    - "cognitive hierarchy theory for distributed resource allocation in the internet of things"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-2 (induced by source paper "data centric mobile crowdsensing"):
  with members:
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "two sided matching based cooperative spectrum sharing"
    - "economic analysis of crowdsourced wireless community networks"
    - "dynamic routing for social information sharing"
    - "providing long term participation incentive in participatory sensing"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

Connected through hyperedge HE-3 (induced by source paper "quality of information in mobile crowdsensing survey and research challenges"):
  with members:
    - "taskme multi task allocation in mobile crowd sensing"
    - "sacrm social aware crowdsourcing with reputation management in mobile sensing"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "a survey on truth discovery"
    - "mining users significant driving routes with low power sensors"
    - "ear phone a context aware noise mapping using smart phones"
    - "a trust based recruitment framework for multi hop social participatory sensing"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-4 (induced by source paper "theseus incentivizing truth discovery in mobile crowd sensing systems"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "providing long term participation incentive in participatory sensing"
    - "crowdsourcing with tullock contests a new perspective"
    - "truthful linear regression"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "linear regression as a non cooperative game"

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.GT(Computer Science and Game Theory)