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 endogenous entry"
  Abstract: We investigate the design of mechanisms to incentivize high quality in crowdsourcing environments with strategic agents, when entry is an endogenous, strategic choice. Modeling endogenous entry in crowdsourcing is important because there is a nonzero cost to making a contribution of any quality which can be avoided by not participating, and indeed many sites based on crowdsourced content do not have adequate participation. We use a mechanism with monotone, rank-based, rewards in a model where agents strategically make participation and quality choices to capture a wide variety of crowdsourcing environments, ranging from conventional crowdsourcing contests to crowdsourced content as in online Q&A forums. #R##N#We first explicitly construct the unique mixed-strategy equilibrium for such monotone rank-order mechanisms, and use these participation probabilities and quality distribution to address the design of incentives for two kinds of rewards that arise in crowdsourcing. We first show that for attention rewards as in crowdsourced content, the entire equilibrium distribution improves when the rewards for every rank but the last are as high as possible. In particular, when producing the lowest quality content has low cost, the optimal mechanism displays all but the worst contribution. We next investigate settings where there is a total reward that can be arbitrarily distributed amongst all participants, as in crowdsourcing contests. Unlike with exogenous entry, here the expected number of participants can be increased by subsidizing entry, which could potentially improve the expected quality of the best contribution. However, we show that free entry is dominated by taxing entry- making all entrants pay a small fee, which is rebated to the winner along with whatever rewards were already assigned, can improve the quality of the best contribution over a winner-take-all contest with no taxes.

Connected through hyperedge HE-1 (induced by source paper "optimal mechanisms with budget for user generated contents"):
  with members:
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-2 (induced by source paper "behavioral mechanism design optimal contests for simple agents"):
  with members:
    - "social status and badge design"
    - "optimal crowdsourcing contests"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-3 (induced by source paper "social status and badge design"):
  with members:
    - "quizz targeted crowdsourcing with a billion potential users"
    - "engaging with massive online courses"
    - "incentives and efficiency in uncertain collaborative environments"
    - "implementing optimal outcomes in social computing a game theoretic approach"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-4 (induced by source paper "incentives and efficiency in uncertain collaborative environments"):
  with members:
    - "bayesian games and the smoothness framework"
    - "implementing optimal outcomes in social computing a game theoretic approach"
    - "optimal crowdsourcing contests"
    - "the cooperative game theory foundations of network bargaining games"

Connected through hyperedge HE-5 (induced by source paper "implementing optimal outcomes in social computing a game theoretic approach"):
  with members:
    - "optimal crowdsourcing contests"

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)