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: "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.

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"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "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 "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"
    - "learning to incentivize eliciting effort via output agreement"
    - "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-3 (induced by source paper "peer loss functions learning from noisy labels without knowing noise rates"):
  with members:
    - "l_dmi an information theoretic noise robust loss function"
    - "water from two rocks maximizing the mutual information"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "an information theoretic framework for designing information elicitation mechanisms that reward truth telling"
    - "learning with symmetric label noise the importance of being unhinged"
    - "deep residual learning for image recognition"
    - "making risk minimization tolerant to label noise"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-4 (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"
    - "learning to incentivize eliciting effort via output agreement"
    - "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-5 (induced by source paper "competitive statistical estimation with strategic data sources"):
  with members:
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "promoting truthful behavior in participatory sensing mechanisms"
    - "estimation with strategic sensors"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "optimal mechanisms for selling information"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-6 (induced by source paper "securely trading unverifiable information without trust"):
  with members:
    - "a scalable verification solution for blockchains"
    - "water from two rocks maximizing the mutual information"
    - "astraea a decentralized blockchain oracle"
    - "betrayal distrust and rationality smart counter collusion contracts for verifiable cloud computing"
    - "smart contract templates essential requirements and design options"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "a new approach to practical active secure two party computation"

Connected through hyperedge HE-7 (induced by source paper "token curated registry with citation graph"):
  with members:
    - "token curated registries a game theoretic approach"
    - "enhancing engagement in token curated registries via an inflationary mechanism"
    - "a memo on the proof of stake mechanism"
    - "peer prediction with heterogeneous tasks"
    - "augur a decentralized open source platform for prediction markets"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "recommendation on academic networks using direction aware citation analysis"

Connected through hyperedge HE-8 (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"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "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"

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)