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 "informed truthfulness in multi task peer prediction"):
  Abstract: The problem of peer prediction is to elicit information from agents in settings without any objective ground truth against which to score reports. Peer prediction mechanisms seek to exploit correlations between signals to align incentives with truthful reports. A long-standing concern has been the possibility of uninformative equilibria. For binary signals, a multi-task mechanism [Dasgupta-Ghosh '13] achieves strong truthfulness, so that the truthful equilibrium strictly maximizes payoff. We characterize conditions on the signal distribution for which this mechanism remains strongly-truthful with non-binary signals, also providing a greatly simplified proof. We introduce the Correlated Agreement (CA) mechanism, which handles multiple signals and provides informed truthfulness: no strategy profile provides more payoff in equilibrium than truthful reporting, and the truthful equilibrium is strictly better than any uninformed strategy (where an agent avoids the effort of obtaining a signal). The CA mechanism is maximally strongly truthful, in that no mechanism in a broad class of mechanisms is strongly truthful on a larger family of signal distributions. We also give a detail-free version of the mechanism that removes any knowledge requirements on the part of the designer, using reports on many tasks to learn statistics while retaining epsilon-informed truthfulness.

Includes members:
  Member "incentivizing evaluation via limited access to ground truth peer prediction makes things worse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "credible sample elicitation")
  Member "putting peer prediction under the micro economic scope and making truth telling focal":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control")
  Member "a framework for designing information elicitation mechanisms that reward truth telling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information gathering with peers submodular optimization with peer prediction constraints")
  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 "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "tuned models of peer assessment in moocs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "key phrase classification in complex assignments")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "surrogate scoring rules")
  Member "mechanisms for making crowds truthful":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)