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: "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.

Connected through hyperedge HE-1 (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"
    - "learning to incentivize eliciting effort via output agreement"
    - "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-2 (induced by source paper "informed truthfulness in multi task peer prediction"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "imagenet large scale visual recognition challenge"
    - "tuned models of peer assessment in moocs"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-3 (induced by source paper "truth serums for massively crowdsourced evaluation tasks"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "approval voting and incentives in crowdsourcing"
    - "tuned models of peer assessment in moocs"
    - "crowdsourced judgement elicitation with endogenous proficiency"

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)