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: "truth serums for massively crowdsourced evaluation tasks"
  Abstract: Incentivizing effort and eliciting truthful responses from agents in the absence of verifiability is a major challenge faced while crowdsourcing many types of evaluation tasks like labeling images, grading assignments in online courses, etc. In this paper, we propose new reward mechanisms for such settings that, unlike most previously studied mechanisms, impose minimal assumptions on the structure and knowledge of the underlying generating model, can account for heterogeneity in the agents' abilities, require no extraneous elicitation from them, and furthermore allow their beliefs to be (almost) arbitrary. Moreover, these mechanisms have the simple and intuitive structure of output agreement mechanisms, which, despite not incentivizing truthful behavior, have nevertheless been quite popular in practice. We achieve this by leveraging a typical characteristic of many of these settings, which is the existence of a large number of similar tasks.

Connected through hyperedge HE-1 (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"
    - "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 "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"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-3 (induced by source paper "partial truthfulness in minimal peer prediction mechanisms with limited knowledge"):
  with members:
    - "peer truth serum incentives for crowdsourcing measurements and opinions"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "learning to incentivize eliciting effort via output agreement"
    - "informed truthfulness in multi task peer prediction"

Connected through hyperedge HE-4 (induced by source paper "avoiding imposters and delinquents adversarial crowdsourcing and peer prediction"):
  with members:
    - "multisection in the stochastic block model using semidefinite programming"
    - "robust collaborative online learning"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "how robust are reconstruction thresholds for community detection"
    - "approval voting and incentives in crowdsourcing"
    - "provably manipulation resistant reputation systems"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-5 (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"
    - "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"
    - "mechanisms for making crowds truthful"
    - "sum of us strategyproof selection from the selectors"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "equilibrium selection in information elicitation without verification via information monotonicity"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "mechanisms for making crowds truthful"

Connected through hyperedge HE-8 (induced by source paper "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"):
  with members:
    - "putting peer prediction under the micro economic scope and making truth telling focal"
    - "informed truthfulness in multi task peer prediction"
    - "methods for ordinal peer grading"

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)