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: "mechanisms for making crowds truthful"
  Abstract: We consider schemes for obtaining truthful reports on a common but hidden signal from large groups of rational, self-interested agents. One example are online feedback mechanisms, where users provide observations about the quality of a product or service so that other users can have an accurate idea of what quality they can expect. However, (i) providing such feedback is costly, and (ii) there are many motivations for providing incorrect feedback.#R##N##R##N#Both problems can be addressed by reward schemes which (i) cover the cost of obtaining and reporting feedback, and (ii) maximize the expected reward of a rational agent who reports truthfully. We address the design of such incentive-compatible rewards for feedback generated in environments with pure adverse selection. Here, the correlation between the true knowledge of an agent and her beliefs regarding the likelihoods of reports of other agents can be exploited to make honest reporting a Nash equilibrium.#R##N##R##N#In this paper we extend existing methods for designing incentive-compatible rewards by also considering collusion. We analyze different scenarios, where, for example, some or all of the agents collude. For each scenario we investigate whether a collusion-resistant, incentive-compatible reward scheme exists, and use automated mechanism design to specify an algorithm for deriving an efficient reward mechanism.

Connected through hyperedge HE-1 (induced by source paper "report sensitive spot checking in peer grading systems"):
  with members:
    - "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"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-2 (induced by source paper "a technical survey on statistical modelling and design methods for crowdsourcing quality control"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "peer prediction with heterogeneous tasks"
    - "optimality of belief propagation for crowdsourced classification"
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "regularized minimax conditional entropy for crowdsourcing"
    - "time sensitive bayesian information aggregation for crowdsourcing systems"
    - "approval voting and incentives in crowdsourcing"
    - "a survey on truth discovery"

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"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "sequential peer prediction learning to elicit effort using posted prices"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-4 (induced by source paper "incentivizing effort and precision in peer grading"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "informed truthfulness in multi task peer prediction"
    - "aggregating partial rankings with applications to peer grading in massive online open courses"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-5 (induced by source paper "inference aided reinforcement learning for incentive mechanism design in crowdsourcing"):
  with members:
    - "argus smartphone enabled human cooperation via multi agent reinforcement learning for disaster situational awareness"
    - "deep reinforcement learning with double q learning"
    - "statistical decision making for optimal budget allocation in crowd labeling"

Connected through hyperedge HE-6 (induced by source paper "incentives for truthful evaluations"):
  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"
    - "the peerrank method for peer assessment"
    - "methods for ordinal peer grading"
    - "tuned models of peer assessment in moocs"
    - "inducing honest reporting without observing outcomes an application to the peer review process"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-7 (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"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-8 (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"
    - "a framework for designing information elicitation mechanisms that reward truth telling"
    - "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"

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)