human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the posted pricing in crowdsourcing power of bonus"):
  Abstract: In practical crowdsourcing systems such as Amazon Mechanical Turk, posted pricing is widely used due to its simplicity, where a task requester publishes a pricing rule a priori, on which workers decide whether to accept and perform the task or not, and are often paid according to the quality of their effort. One of the key ingredients of a good posted pricing lies in how to recruit more high-quality workers with less budget, for which the following two schemes are considered: (i) personalized pricing by profiling users in terms of their quality and cost, and (ii) additional bonus payment offered for more qualified task completion. Despite their potential benefits in crowdsourced pricing, it has been under-explored how much gain each or both of personalization and bonus payment actually provides to the requester. In this paper, we study four possible combinations of posted pricing made by pricing with/without personalization and bonus. We aim at analytically quantifying when and how much such two ideas contribute to the requester's utility. To this end, we first derive the optimal personalized and common pricing schemes and analyze their computational tractability. Next, we quantify the gap in the utility between with and without bonus payment in both pricing schemes. We analytically prove that the impact of bonus is negligible significantly marginal in personalized pricing, whereas crucial in common pricing. Finally, we study the notion of Price of Agnosticity that quantifies the utility gap between personalized and common pricing policies. This implies that a complex personalized pricing with privacy concerns can be replaced by a simple common pricing with bonus. We validate our analytical findings through extensive simulations and real experiments done in Amazon Mechanical Turk, and provide additional implications that are useful in designing a pricing policy.

Includes members:
  Member "optimal posted prices for online cloud resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online inventory management with application to energy procurement in data centers")
  Member "bayesian budget feasibility with posted pricing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "approval voting and incentives in crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins")
  Member "incentivizing high quality crowdwork":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crowdsourcing the perception of machine teaching")
  Member "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "double or nothing multiplicative incentive mechanisms for crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")
  Member "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "combining crowd and machines for multi predicate item screening")
  Member "reputation based incentive protocols in crowdsourcing applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")

Question: Please tell me which class the source paper 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)