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: "conducting truthful surveys cheaply"
  Abstract: We consider the problem of conducting a survey with the goal of obtaining an unbiased estimator of some population statistic when individuals have unknown costs (drawn from a known prior) for participating in the survey. Individuals must be compensated for their participation and are strategic agents, and so the payment scheme must incentivize truthful behavior. We derive optimal truthful mechanisms for this problem for the two goals of minimizing the variance of the estimator given a fixed budget, and minimizing the expected cost of the survey given a fixed variance goal.

Connected through hyperedge HE-1 (induced by source paper "towards data auctions with externalities"):
  with members:
    - "a marketplace for data an algorithmic solution"
    - "low cost learning via active data procurement"
    - "budget feasible mechanisms for experimental design"
    - "selling privacy at auction"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-2 (induced by source paper "prior free data acquisition for accurate statistical estimation"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"
    - "optimal data acquisition for statistical estimation"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "low cost learning via active data procurement"
    - "truthful linear regression"

Connected through hyperedge HE-3 (induced by source paper "optimized direct sale of privacy in personal data marketplaces"):
  with members:
    - "myadchoices bringing transparency and control to online advertising"
    - "you never surf alone ubiquitous tracking of users browsing habits"
    - "a theory of pricing private data"
    - "a market for unbiased private data paying individuals according to their privacy attitudes"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "privacy auctions for recommender systems"
    - "selling privacy at auction"

Connected through hyperedge HE-4 (induced by source paper "optimal data acquisition for statistical estimation"):
  with members:
    - "strategic classification from revealed preferences"
    - "low cost learning via active data procurement"
    - "truthful linear regression"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "selling privacy at auction"

Connected through hyperedge HE-5 (induced by source paper "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"):
  with members:
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "privacy games optimal user centric data obfuscation"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "buying private data without verification"
    - "on the relation between identifiability differential privacy and mutual information privacy"

Connected through hyperedge HE-6 (induced by source paper "learning to incentivize eliciting effort via output agreement"):
  with members:
    - "informed truthfulness in multi task peer prediction"
    - "incentivizing high quality crowdwork"
    - "elicitation for aggregation"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-7 (induced by source paper "sequential peer prediction learning to elicit effort using posted prices"):
  with members:
    - "learning to incentivize eliciting effort via output agreement"
    - "mechanism design for data science"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-8 (induced by source paper "a game theoretic study on non monetary incentives in data analytics projects with privacy implications"):
  with members:
    - "budget feasible mechanisms for experimental design"
    - "what s the gist privacy preserving aggregation of user profiles"
    - "linear regression as a non cooperative game"
    - "crowd assisted search for price discrimination in e commerce first results"
    - "take it or leave it running a survey when privacy comes at a cost"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"
    - "approximately optimal mechanism design via differential privacy"

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)