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 "competitive statistical estimation with strategic data sources"):
  Abstract: In recent years, data has played an increasingly important role in the economy as a good in its own right. In many settings, data aggregators cannot directly verify the quality of the data they purchase, nor the effort exerted by data sources when creating the data. Recent work has explored mechanisms to ensure that the data sources share high quality data with a single data aggregator, addressing the issue of moral hazard. Oftentimes, there is a unique, socially efficient solution. #R##N#In this paper, we consider data markets where there is more than one data aggregator. Since data can be cheaply reproduced and transmitted once created, data sources may share the same data with more than one aggregator, leading to free-riding between data aggregators. This coupling can lead to non-uniqueness of equilibria and social inefficiency. We examine a particular class of mechanisms that have received study recently in the literature, and we characterize all the generalized Nash equilibria of the resulting data market. We show that, in contrast to the single-aggregator case, there is either infinitely many generalized Nash equilibria or none. We also provide necessary and sufficient conditions for all equilibria to be socially inefficient. In our analysis, we identify the components of these mechanisms which give rise to these undesirable outcomes, showing the need for research into mechanisms for competitive settings with multiple data purchasers and sellers.

Includes members:
  Member "optimal data acquisition for statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what is the value of data on mathematical methods for data quality estimation")
  Member "statistical estimation with strategic data sources in competitive settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rewarding high quality data via influence functions")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "surrogate scoring rules")
  Member "promoting truthful behavior in participatory sensing mechanisms":
    also connected through other hyperedges:
      (none)
  Member "estimation with strategic sensors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic information design a simple problem on optimal sequential information disclosure")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surrogate scoring rules")
  Member "optimal mechanisms for selling information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "computing the value of spatio temporal data in wholesale and retail data marketplaces")
  Member "optimal crowdsourcing contests":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "collaborative data acquisition")

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)