human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "surrogate scoring rules"):
  Abstract: Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher score in expectation. In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution \emph{Surrogate Scoring Rules} (SSR). SSR build on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with one bit of information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the {\em with} ground truth setting. As a by-product, SSR induce \emph{dominant truthfulness} in reporting.  Our work complements the proper scoring rule literature via extending existing SPSR to operate when there is no clean ground truth verification. Because of the non-existence of verification, our setting falls into the classical information elicitation without verification (IEWV) domain, which has focused on eliciting discrete signals. Therefore our work also contributes to the peer prediction literature via providing a scoring rule that elicits continuous probabilistic beliefs, an approach that rewards accuracy instead of correlation, and a mechanism that achieves truthfulness in \emph{dominant strategy} in a multi-task setting. Our method is verified both theoretically and empirically using data collected from real human forecasters.

Includes members:
  Member "deep bayesian trust a dominant and fair incentive mechanism for crowd":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "farm fair reward mechanism for information aggregation in spontaneous localized settings extended version")
  Member "water from two rocks maximizing the mutual information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "incentivizing evaluation via limited access to ground truth peer prediction makes things worse":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "credible sample elicitation")
  Member "incentives for truthful peer grading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "credible sample elicitation")
  Member "informed truthfulness in multi task peer prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prior free data acquisition for accurate statistical estimation")
  Member "equilibrium selection in information elicitation without verification via information monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dominantly truthful multi task peer prediction with a constant number of tasks")
  Member "an information theoretic framework for designing information elicitation mechanisms that reward truth telling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "credible sample elicitation")
  Member "crowdsourced judgement elicitation with endogenous proficiency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "peer loss functions learning from noisy labels without knowing noise rates")

Question: Please tell me which class the source paper 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)