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: "optimal prizes for all pay contests in heterogeneous crowdsourcing"
  Abstract: Incentive is key to the success of crowd sourcing which heavily depends on the level of user participation. This paper designs an incentive mechanism to motivate a heterogeneous crowd of users to actively participate in crowd sourcing campaigns. We cast the problem in a new, asymmetric all-pay contest model with incomplete information, where an arbitrary n of users exert irrevocable effort to compete for a prize tuple. The prize tuple is an array of prize functions as opposed to a single constant prize typically used by conventional contests. We design an optimal contest that (a) induces the maximum profit -- total user effort minus the prize payout -- for the crowdsourcer, and (b) ensures users to strictly have incentive to participate. In stark contrast to intuition and prior related work, our mechanism induces an equilibrium in which heterogeneous users behave independently of one another as if they were in a homogeneous setting. This newly discovered property, which we coin as strategy autonomy (SA), is of practical significance: it (a) reduces computational and storage complexity by n-fold for each user, (b) increases the crowdsourcer's revenue by counteracting an effort reservation effect existing in asymmetric contests, and (c) neutralizes the (almost universal) law of diminishing marginal returns (DMR). Through an extensive numerical case study, we demonstrate and scrutinize the superior profitability of our mechanism, as well as draw insights into the SA property.

Connected through hyperedge HE-1 (induced by source paper "simple credible and approximately optimal auctions"):
  with members:
    - "an improved welfare guarantee for first price auctions"
    - "learning multi item auctions with or without samples"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "sample complexity for non truthful mechanisms"
    - "the price of anarchy in auctions"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

Connected through hyperedge HE-2 (induced by source paper "optimal mechanisms with budget for user generated contents"):
  with members:
    - "crowdsourcing with endogenous entry"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-3 (induced by source paper "rating protocol design for extortion and cooperation in the crowdsourcing contest dilemma"):
  with members:
    - "when reputation enforces evolutionary cooperation in unreliable manets"
    - "iterated crowdsourcing dilemma game"
    - "socially optimal design of service exchange platforms with imperfect monitoring"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "medium access control protocols with memory"

Connected through hyperedge HE-4 (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"
    - "crowdsourcing with tullock contests a new perspective"

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"
    - "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"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "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:
    - "wifiscout a crowdsensing wifi advisory system with gamification based incentive"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-7 (induced by source paper "an efficient and truthful pricing mechanism for team formation in crowdsourcing markets"):
  with members:
    - "budget feasible mechanisms"

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.MA(Multiagent Systems)