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: "double or nothing multiplicative incentive mechanisms for crowdsourcing"
  Abstract: Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental challenge in crowdsourcing, we propose a simple payment mechanism to incentivize workers to answer only the questions that they are sure of and skip the rest. We show that surprisingly, under a mild and natural "no-free-lunch" requirement, this mechanism is the one and only incentive-compatible payment mechanism possible. We also show that among all possible incentive-compatible mechanisms (that may or may not satisfy no-free-lunch), our mechanism makes the smallest possible payment to spammers. We further extend our results to a more general setting in which workers are required to provide a quantized confidence for each question. Interestingly, this unique mechanism takes a "multiplicative" form. The simplicity of the mechanism is an added benefit. In preliminary experiments involving over 900 worker-task pairs, we observe a significant drop in the error rates under this unique mechanism for the same or lower monetary expenditure.

Connected through hyperedge HE-1 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers"):
  with members:
    - "decision tree design for classification in crowdsourcing systems"
    - "optimal crowdsourced classification with a reject option in the presence of spammers"
    - "does confidence reporting from the crowd benefit crowdsourcing performance"
    - "multi object classification via crowdsourcing with a reject option"
    - "reliable crowdsourcing for multi class labeling using coding theory"
    - "reputation based incentive protocols in crowdsourcing applications"
    - "serf and turf crowdturfing for fun and profit"

Connected through hyperedge HE-2 (induced by source paper "truth discovery via proxy voting"):
  with members:
    - "a survey on truth discovery"
    - "truth discovery algorithms an experimental evaluation"
    - "common voting rules as maximum likelihood estimators"

Connected through hyperedge HE-3 (induced by source paper "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"):
  with members:
    - "crowd guilds worker led reputation and feedback on crowdsourcing platforms"
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "optimality of belief propagation for crowdsourced classification"
    - "embracing error to enable rapid crowdsourcing"
    - "crowd access path optimization diversity matters"
    - "incentivizing high quality crowdwork"
    - "quizz targeted crowdsourcing with a billion potential users"
    - "how many workers to ask adaptive exploration for collecting high quality labels"

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"
    - "approval voting and incentives in crowdsourcing"
    - "a survey on truth discovery"

Connected through hyperedge HE-5 (induced by source paper "millionaire a hint guided approach for crowdsourcing"):
  with members:
    - "end to end learning for self driving cars"
    - "crowdsourcing with unsure option"
    - "on the convergence of a family of robust losses for stochastic gradient descent"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "incentivizing high quality crowdwork"
    - "training convolutional networks with noisy labels"
    - "imagenet large scale visual recognition challenge"
    - "evaluating the crowd with confidence"

Connected through hyperedge HE-6 (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"
    - "approval voting and incentives in crowdsourcing"
    - "incentivizing high quality crowdwork"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "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-7 (induced by source paper "zebralancer crowdsource knowledge atop open blockchain privately and anonymously"):
  with members:
    - "sentiment protocol a decentralized protocol leveraging crowd sourced wisdom"
    - "enigma decentralized computation platform with guaranteed privacy"
    - "reputation based incentive protocols in crowdsourcing applications"

Connected through hyperedge HE-8 (induced by source paper "zebralancer private and anonymous crowdsourcing system atop open blockchain"):
  with members:
    - "sentiment protocol a decentralized protocol leveraging crowd sourced wisdom"
    - "enigma decentralized computation platform with guaranteed privacy"
    - "reputation based incentive protocols in crowdsourcing applications"

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)