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: "from participatory sensing to mobile crowd sensing"
  Abstract: The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

Connected through hyperedge HE-1 (induced by source paper "trust evaluation mechanism for user recruitment in mobile crowd sensing in the internet of things"):
  with members:
    - "social network assisted worker recruitment in mobile crowd sensing"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "when crowdsourcing meets mobile sensing a social network perspective"
    - "mobile crowd sensing and computing when participatory sensing meets participatory social media"
    - "sensing as a service model for smart cities supported by internet of things"
    - "internet of things iot a vision architectural elements and future directions"
    - "pagerank standing on the shoulders of giants"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-2 (induced by source paper "resource management in cloud networking using economic analysis and pricing models a survey"):
  with members:
    - "network function virtualization state of the art and research challenges"
    - "fronthaul constrained cloud radio access networks insights and challenges"
    - "distributed cloud association in downlink multicloud radio access networks"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "an auction mechanism for resource allocation in mobile cloud computing systems"
    - "behavior based online incentive mechanism for crowd sensing with budget constraints"
    - "internet resource pricing models mechanisms and methods"
    - "budget feasible mechanisms"

Connected through hyperedge HE-3 (induced by source paper "safernet safe transportation routing in the era of internet of vehicles and mobile crowd sensing"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-4 (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"
    - "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"
    - "incentives for privacy tradeoff in community sensing"

Connected through hyperedge HE-5 (induced by source paper "on mobile cloud for smart city applications"):
  with members:
    - "cloud services brokerage a survey and research roadmap"
    - "sensing as a service model for smart cities supported by internet of things"
    - "mobile cloud computing a comparison of application models"

Connected through hyperedge HE-6 (induced by source paper "wirelessly powered urban crowd sensing over wearables trading energy for data"):
  with members:
    - "sustainable incentives for mobile crowdsensing auctions lotteries and trust and reputation systems"
    - "ambient rf energy harvesting in ultra dense small cell networks performance and trade offs"
    - "on feasibility of 5g grade dedicated rf charging technology for wireless powered wearables"

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.HC(Human-Computer Interaction)