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 "a framework for designing information elicitation mechanisms that reward truth telling"):
  Abstract: Information elicitation mechanisms, such as Peer Prediction [Miller 2005] and Bayesian Truth Serum [Prelec 2004], are designed to reward agents for honestly reporting their private information, even when this information cannot be directly verified. Information elicitation mechanisms, such as these, are cleverly designed so that truth-telling is a strict Bayesian Nash Equilibrium. However, a key challenge that has arisen is that there are typically many other non-truthful equilibrium as well, and it is important that truth-telling not only be an equilibrium, but be paid more than other equilibrium so that agents do not coordinate on a non-informative equilibria. Several past works have overcome this challenge in various setting using clever techniques, but a new technique was required for each new setting. #R##N#Our main contribution in this paper is providing a framework for designing information elicitation mechanisms where truth-telling is the highest paid equilibrium, even when the mechanism does not know the common prior. We define information monotone functions which can measure the amount of "information" contained in agents' reports such that the function is greater in the truthful equilibrium than in non-truthful equilibria. We provide several interesting information monotone functions ( f -disagreement, f-mutual information, f -information gain) in different settings. Aided by these theoretical tools, we (1) extend Dasgupta and Ghosh[2013]'s mechanism to the non-binary setting with an additional assumption that agents are asked to answer a large number of a priori similar questions; (2) reprove the main results of Prelec[2004], Dasgupta and Ghosh[2013] and a weaker version of Kong and Schoenebeck[2016] in our framework. Our framework thus provides both new mechanisms and a deeper understanding of prior results.

Includes members:
  Member "peer prediction with heterogeneous tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "surrogate scoring rules")
  Member "equilibrium selection in information elicitation without verification via information monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "surrogate scoring rules")
  Member "truth serums for massively crowdsourced evaluation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "surrogate scoring rules")
  Member "mechanisms for making crowds truthful":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "report sensitive spot checking in peer grading systems")

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)