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 socially aware incentive mechanism for mobile crowdsensing service market"
  Abstract: Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.

Connected through hyperedge HE-1 (induced by source paper "a novel methodology for designing policies in mobile crowdsensing systems"):
  with members:
    - "incentme effective mechanism design to stimulate crowdsensing participants with uncertain mobility"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "incentive mechanisms for participatory sensing survey and research challenges"

Connected through hyperedge HE-2 (induced by source paper "dynamic pricing for revenue maximization in mobile social data market with network effects"):
  with members:
    - "mobile data trading behavioral economics analysis and algorithm design"
    - "competition and cooperation analysis for data sponsored market a network effects model"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "price discrimination for energy trading in smart grid a game theoretic approach"
    - "time and location aware mobile data pricing"
    - "a survey of smart data pricing past proposals current plans and future trends"
    - "optimal pricing in networks with externalities"
    - "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach"

Connected through hyperedge HE-3 (induced by source paper "a stackelberg game approach towards socially aware incentive mechanisms for mobile crowdsensing online report"):
  with members:
    - "dynamic routing for social information sharing"
    - "crowdsourcing with tullock contests a new perspective"
    - "optimal pricing in networks with externalities"
    - "optimal pricing in social networks with incomplete information"

Connected through hyperedge HE-4 (induced by source paper "game theoretic analysis for joint sponsored and edge caching content service market"):
  with members:
    - "competition and cooperation analysis for data sponsored market a network effects model"
    - "joint optimization of data sponsoring and edge caching for mobile video delivery"
    - "caching in wireless small cell networks a storage bandwidth tradeoff"
    - "competitive caching of contents in 5g edge cloud networks"

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)