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 "prior free data acquisition for accurate statistical estimation"):
  Abstract: We study a data analyst's problem of acquiring data from self-interested individuals to obtain an accurate estimation of some statistic of a population, subject to an expected budget constraint. Each data holder incurs a cost, which is unknown to the data analyst, to acquire and report his data. The cost can be arbitrarily correlated with the data. The data analyst has an expected budget that she can use to incentivize individuals to provide their data. The goal is to design a joint acquisition-estimation mechanism to optimize the performance of the produced estimator, without any prior information on the underlying distribution of cost and data. We investigate two types of estimations: unbiased point estimation and confidence interval estimation.   Unbiased estimators: We design a truthful, individually rational, online mechanism to acquire data from individuals and output an unbiased estimator of the population mean when the data analyst has no prior information on the cost-data distribution and individuals arrive in a random order. The performance of this mechanism matches that of the optimal mechanism, which knows the true cost distribution, within a constant factor. The performance of an estimator is evaluated by its variance under the worst-case cost-data correlation.   Confidence intervals: We characterize an approximately optimal (within a factor 2) mechanism for obtaining a confidence interval of the population mean when the data analyst knows the true cost distribution at the beginning. This mechanism is efficiently computable. We then design a truthful, individually rational, online algorithm that is only worse than the approximately optimal mechanism by a constant factor. The performance of an estimator is evaluated by its expected length under the worst-case cost-data correlation.

Includes members:
  Member "strategyproof linear regression in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mechanism design for multi party machine learning")
  Member "strategic classification from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "optimal data acquisition for statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "what is the value of data on mathematical methods for data quality estimation")
  Member "learning to incentivize eliciting effort via output agreement":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "impact of social learning on privacy preserving data collection")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surrogate scoring rules")
  Member "sequential peer prediction learning to elicit effort using posted prices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "eliciting expertise without verification")
  Member "low cost learning via active data procurement":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards data auctions with externalities")
  Member "truthful linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "causal feature discovery through strategic modification")

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)