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: "crowdsourced judgement elicitation with endogenous proficiency"
  Abstract: Crowdsourcing is now widely used to replace judgement by an expert authority with an aggregate evaluation from a number of non-experts, in applications ranging from rating and categorizing online content to evaluation of student assignments in massively open online courses via peer grading. A key issue in these settings, where direct monitoring is infeasible, is incentivizing agents in the `crowd' to put in effort to make good evaluations, as well as to truthfully report their evaluations. This leads to a new family of information elicitation problems with unobservable ground truth, where an agent's proficiency- the probability with which she correctly evaluates the underlying ground truth- is endogenously determined by her strategic choice of how much effort to put into the task. #R##N#Our main contribution is a simple, new, mechanism for binary information elicitation for multiple tasks when agents have endogenous proficiencies, with the following properties: (i) Exerting maximum effort followed by truthful reporting of observations is a Nash equilibrium. (ii) This is the equilibrium with maximum payoff to all agents, even when agents have different maximum proficiencies, can use mixed strategies, and can choose a different strategy for each of their tasks. Our information elicitation mechanism requires only minimal bounds on the priors, asks agents to only report their own evaluations, and does not require any conditions on a diverging number of agent reports per task to achieve its incentive properties. The main idea behind our mechanism is to use the presence of multiple tasks and ratings to identify and penalize low-effort agreement: the mechanism rewards agents for agreeing with a `reference' rater on a task but also penalizes for blind agreement by subtracting out a statistic term designed so that agents obtain reward only when they put effort into their observations.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "informed truthfulness in multi task peer prediction"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "lie on the fly strategic voting in an iterative preference elicitation process"):
  with members:
    - "cognitive hierarchy and voting manipulation"
    - "online prediction with selfish experts"
    - "a local dominance theory of voting equilibria"
    - "the computational impact of partial votes on strategic voting"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "dominating manipulations in voting with partial information"
    - "equilibria of plurality voting with abstentions"
    - "where are the hard manipulation problems"

Connected through hyperedge HE-4 (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"
    - "informed truthfulness in multi task peer prediction"
    - "promoting truthful behavior in participatory sensing mechanisms"
    - "estimation with strategic sensors"
    - "optimal mechanisms for selling information"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-5 (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"
    - "informed truthfulness in multi task peer prediction"
    - "a new approach to practical active secure two party computation"

Connected through hyperedge HE-6 (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"
    - "informed truthfulness in multi task peer prediction"
    - "augur a decentralized open source platform for prediction markets"
    - "recommendation on academic networks using direction aware citation analysis"

Connected through hyperedge HE-7 (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"
    - "truth serums for massively crowdsourced evaluation tasks"

Connected through hyperedge HE-8 (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"
    - "mechanisms for making crowds truthful"

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)