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: "approval voting and incentives in crowdsourcing"
  Abstract: The growing need for labeled training data has made crowdsourcing an important part of machine learning. The quality of crowdsourced labels is, however, adversely affected by three factors: (1) the workers are not experts; (2) the incentives of the workers are not aligned with those of the requesters; and (3) the interface does not allow workers to convey their knowledge accurately, by forcing them to make a single choice among a set of options. In this paper, we address these issues by introducing approval voting to utilize the expertise of workers who have partial knowledge of the true answer, and coupling it with a ("strictly proper") incentive-compatible compensation mechanism. We show rigorous theoretical guarantees of optimality of our mechanism together with a simple axiomatic characterization. We also conduct preliminary empirical studies on Amazon Mechanical Turk which validate our approach.

Connected through hyperedge HE-1 (induced by source paper "uncertainty about uncertainty near optimal adaptive algorithms for estimating binary mixtures of unknown coins"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "beyond talagrand functions new lower bounds for testing monotonicity and unateness"
    - "learning populations of parameters"
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "a data quality metric dqm how to estimate the number of undetected errors in data sets"
    - "a polynomial lower bound for testing monotonicity"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "testing probability distributions underlying aggregated data"

Connected through hyperedge HE-2 (induced by source paper "rewarding high quality data via influence functions"):
  with members:
    - "competitive statistical estimation with strategic data sources"
    - "towards efficient data valuation based on the shapley value"
    - "federated learning strategies for improving communication efficiency"
    - "strategyproof linear regression in high dimensions"
    - "optimal data acquisition for statistical estimation"
    - "statistical estimation with strategic data sources in competitive settings"
    - "truthful linear regression"

Connected through hyperedge HE-3 (induced by source paper "practice of efficient data collection via crowdsourcing at large scale"):
  with members:
    - "regularized minimax conditional entropy for crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "how many workers to ask adaptive exploration for collecting high quality labels"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "learning to predict the wisdom of crowds"

Connected through hyperedge HE-4 (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"
    - "a survey on truth discovery"
    - "quizz targeted crowdsourcing with a billion potential users"

Connected through hyperedge HE-5 (induced by source paper "on the posted pricing in crowdsourcing power of bonus"):
  with members:
    - "optimal posted prices for online cloud resource allocation"
    - "bayesian budget feasibility with posted pricing"
    - "incentivizing high quality crowdwork"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-6 (induced by source paper "adaptive matching for expert systems with uncertain task types"):
  with members:
    - "matching while learning"
    - "enhancing the delay performance of dynamic backpressure algorithms"
    - "near optimal bayesian active learning for decision making"
    - "delay based back pressure scheduling in multihop wireless networks"
    - "backpressure with adaptive redundancy bwar"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-7 (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"
    - "truth serums for massively crowdsourced evaluation tasks"
    - "how robust are reconstruction thresholds for community detection"
    - "provably manipulation resistant reputation systems"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-8 (induced by source paper "repeated matching mechanism design with moral hazard and adverse selection"):
  with members:
    - "incentive design in peer review rating and repeated endogenous matching"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "budget optimal task allocation for reliable crowdsourcing systems"

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)