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: "equilibrium selection in information elicitation without verification via information monotonicity"
  Abstract: Peer-prediction is a mechanism which elicits privately-held, non-variable information from self-interested agents---formally, truth-telling is a strict Bayes Nash equilibrium of the mechanism. The original Peer-prediction mechanism suffers from two main limitations: (1) the mechanism must know the "common prior" of agents' signals; (2) additional undesirable and non-truthful equilibria exist which often have a greater expected payoff than the truth-telling equilibrium. A series of results has successfully weakened the known common prior assumption. However, the equilibrium multiplicity issue remains a challenge. #R##N#In this paper, we address the above two problems. In the setting where a common prior exists but is not known to the mechanism we show (1) a general negative result applying to a large class of mechanisms showing truth-telling can never pay strictly more in expectation than a particular set of equilibria where agents collude to "relabel" the signals and tell the truth after relabeling signals; (2) provide a mechanism that has no information about the common prior but where truth-telling pays as much in expectation as any relabeling equilibrium and pays strictly more than any other symmetric equilibrium; (3) moreover in our mechanism, if the number of agents is sufficiently large, truth-telling pays similarly to any equilibrium close to a "relabeling" equilibrium and pays strictly more than any equilibrium that is not close to a relabeling equilibrium.

Connected through hyperedge HE-1 (induced by source paper "surrogate scoring rules"):
  with members:
    - "deep bayesian trust a dominant and fair incentive mechanism for crowd"
    - "water from two rocks maximizing the mutual information"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "incentives for truthful peer grading"
    - "informed truthfulness in multi task peer prediction"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-2 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks"):
  with members:
    - "eliciting expertise without verification"
    - "water from two rocks maximizing the mutual information"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"

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"
    - "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"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-4 (induced by source paper "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"):
  with members:
    - "eliciting expertise without verification"
    - "water from two rocks maximizing the mutual information"
    - "peer prediction with heterogeneous tasks"
    - "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"
    - "informed truthfulness in multi task peer prediction"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-5 (induced by source paper "a framework for designing information elicitation mechanisms that reward truth telling"):
  with members:
    - "peer prediction with heterogeneous tasks"
    - "informed truthfulness in multi task peer prediction"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "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)