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: "learning to incentivize eliciting effort via output agreement"
  Abstract: In crowdsourcing when there is a lack of verification for contributed answers, output agreement mechanisms are often used to incentivize participants to provide truthful answers when the correct answer is hold by the majority. In this paper, we focus on using output agreement mechanisms to elicit effort, in addition to eliciting truthful answers, from a population of workers. We consider a setting where workers have heterogeneous cost of effort exertion and examine the data requester's problem of deciding the reward level in output agreement for optimal elicitation. In particular, when the requester knows the cost distribution, we derive the optimal reward level for output agreement mechanisms. This is achieved by first characterizing Bayesian Nash equilibria of output agreement mechanisms for a given reward level. When the requester does not know the cost distribution, we develop sequential mechanisms that combine learning the cost distribution with incentivizing effort exertion to approximately determine the optimal reward level.

Connected through hyperedge HE-1 (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"
    - "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"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"

Connected through hyperedge HE-2 (induced by source paper "impact of social learning on privacy preserving data collection"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "informed truthfulness in multi task peer prediction"
    - "redrawing the boundaries on purchasing data from privacy sensitive individuals"
    - "approximately optimal auctions for selling privacy when costs are correlated with data"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"

Connected through hyperedge HE-3 (induced by source paper "eliciting expertise without verification"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "informed truthfulness in multi task peer prediction"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-4 (induced by source paper "information gathering with peers submodular optimization with peer prediction constraints"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "a framework for designing information elicitation mechanisms that reward truth telling"
    - "crowd access path optimization diversity matters"
    - "information gathering in networks via active exploration"
    - "incentives for privacy tradeoff in community sensing"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "a utility framework for bounded loss market makers"

Connected through hyperedge HE-5 (induced by source paper "partial truthfulness in minimal peer prediction mechanisms with limited knowledge"):
  with members:
    - "peer truth serum incentives for crowdsourcing measurements and opinions"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "truth serums for massively crowdsourced evaluation tasks"

Connected through hyperedge HE-6 (induced by source paper "sequential peer prediction learning to elicit effort using posted prices"):
  with members:
    - "mechanism design for data science"
    - "conducting truthful surveys cheaply"
    - "mechanisms for making crowds truthful"

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)