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: "fairness and social welfare in incentivizing participatory sensing"
  Abstract: Participatory sensing has emerged recently as a promising approach to large-scale data collection. However, without incentives for users to regularly contribute good quality data, this method is unlikely to be viable in the long run. In this paper, we link incentive to users' demand for consuming compelling services, as an approach complementary to conventional credit or reputation based approaches. With this demand-based principle, we design two incentive schemes, Incentive with Demand Fairness (IDF) and Iterative Tank Filling (ITF), for maximizing fairness and social welfare, respectively. Our study shows that the IDF scheme is max-min fair and can score close to 1 on the Jain's fairness index, while the ITF scheme maximizes social welfare and achieves a unique Nash equilibrium which is also Pareto and globally optimal. We adopted a game theoretic approach to derive the optimal service demands. Furthermore, to address practical considerations, we use a stochastic programming technique to handle uncertainty that is often encountered in real life situations.

Connected through hyperedge HE-1 (induced by source paper "a double auction mechanism for mobile crowd sensing with data reuse"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "computationally feasible vcg mechanisms"

Connected through hyperedge HE-2 (induced by source paper "community networks and sustainability a survey of perceptions practices and proposed solutions"):
  with members:
    - "bottom up broadband initiatives in the commons for europe project"
    - "community cloud computing"

Connected through hyperedge HE-3 (induced by source paper "cheating resilient incentive scheme for mobile crowdsensing systems"):
  with members:
    - "on cost effective incentive mechanisms in microtask crowdsourcing"

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"
    - "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"
    - "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 "incentive mechanisms for participatory sensing survey and research challenges"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "sacrm social aware crowdsourcing with reputation management in mobile sensing"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "the importance of being earnest in crowdsourcing systems"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "mining users significant driving routes with low power sensors"
    - "truthful scheduling mechanisms for powering mobile crowdsensing"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "crowdsourcing with tullock contests a new perspective"):
  with members:
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "wifiscout a crowdsensing wifi advisory system with gamification based incentive"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-7 (induced by source paper "sacrm social aware crowdsourcing with reputation management in mobile sensing"):
  with members:
    - "a trust based recruitment framework for multi hop social participatory sensing"
    - "reputation based incentive protocols in crowdsourcing applications"

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)