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: "crowdsourcing with tullock contests a new perspective"
  Abstract: Incentive mechanisms for crowdsourcing have been extensively studied under the framework of all-pay auctions. Along a distinct line, this paper proposes to use Tullock contests as an alternative tool to design incentive mechanisms for crowdsourcing. We are inspired by the conduciveness of Tullock contests to attracting user entry (yet not necessarily a higher revenue) in other domains. In this paper, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize — which is fixed in conventional Tullock contests — with a prize function that is dependent on the (unknown) winner's contribution, in order to maximize the crowdsourcer's utility. We show that this approach leads to attractive practical advantages: (a) it is well-suited for rapid prototyping in fully distributed web agents and smartphone apps; (b) it overcomes the disincentive to participate caused by players' antagonism to an increasing number of rivals. Furthermore, we optimize conventional, fixed-prize Tullock contests to construct the most superior benchmark to compare against our mechanism. Through extensive evaluations, we show that our mechanism significantly outperforms the optimal benchmark, by over three folds on the crowdsourcer's utility cum profit and up to nine folds on the players' social welfare.

Connected through hyperedge HE-1 (induced by source paper "small profits and quick returns an incentive mechanism design for iot based crowdsourcing under continuous platform competition"):
  with members:
    - "providing long term participation incentive in participatory sensing"

Connected through hyperedge HE-2 (induced by source paper "a stackelberg game approach towards socially aware incentive mechanisms for mobile crowdsensing online report"):
  with members:
    - "a socially aware incentive mechanism for mobile crowdsensing service market"
    - "dynamic routing for social information sharing"
    - "optimal pricing in networks with externalities"
    - "optimal pricing in social networks with incomplete information"

Connected through hyperedge HE-3 (induced by source paper "reap an efficient incentive mechanism for reconciling aggregation accuracy and individual privacy in crowdsensing"):
  with members:
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "privacy and mechanism design"
    - "calibrating data to sensitivity in private data analysis"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

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

Connected through hyperedge HE-5 (induced by source paper "reshaping mobile crowd sensing using cross validation to improve data credibility"):
  with members:
    - "sustainable incentives for mobile crowdsensing auctions lotteries and trust and reputation systems"
    - "the privacy exposure problem in mobile location based services"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "a socially aware incentive mechanism for mobile crowdsensing service market"):
  with members:
    - "how peer effects influence energy consumption"
    - "competition and cooperation analysis for data sponsored market a network effects model"
    - "optimal pricing in networks with externalities"

Connected through hyperedge HE-7 (induced by source paper "centurion incentivizing multi requester mobile crowd sensing"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "distributed time sensitive task selection in mobile crowdsensing"

Connected through hyperedge HE-8 (induced by source paper "sustainable incentives for mobile crowdsensing auctions lotteries and trust and reputation systems"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

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)