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: "eliciting forecasts from self interested experts scoring rules for decision makers"
  Abstract: Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions (e.g., score awarded by the rule, or payoff in a prediction market). We study a more realistic setting in which (a) the principal is a decision maker and will take a decision based on the expert's prediction; and (b) the expert has an inherent interest in the decision. For example, in a corporate decision market, the expert may derive different levels of utility from the actions taken by her manager. As a consequence the expert will usually have an incentive to misreport her forecast to influence the choice of the decision maker if typical scoring rules are used. We develop a general model for this setting and introduce the concept of a compensation rule. When combined with the expert's inherent utility for decisions, a compensation rule induces a net scoring rule that behaves like a normal scoring rule. Assuming full knowledge of expert utility, we provide a complete characterization of all (strictly) proper compensation rules. We then analyze the situation where the expert's utility function is not fully known to the decision maker. We show bounds on: (a) expert incentive to misreport; (b) the degree to which an expert will misreport; and (c) decision maker loss in utility due to such uncertainty. These bounds depend in natural ways on the degree of uncertainty, the local degree of convexity of net scoring function, and natural properties of the decision maker's utility function. They also suggest optimization procedures for the design of compensation rules. Finally, we briefly discuss the use of compensation rules as market scoring rules for self-interested experts in a prediction market.

Connected through hyperedge HE-1 (induced by source paper "on manipulation in prediction markets when participants influence outcomes directly"):
  with members:
    - "a utility framework for bounded loss market makers"
    - "an optimization based framework for automated market making"

Connected through hyperedge HE-2 (induced by source paper "team selection for prediction tasks"):
  with members:
    - "an empirical comparison of algorithms for aggregating expert predictions"

Connected through hyperedge HE-3 (induced by source paper "designing markets for daily deals"):
  with members:
    - "the groupon effect on yelp ratings a root cause analysis"
    - "daily deals prediction social diffusion and reputational ramifications"

Connected through hyperedge HE-4 (induced by source paper "general truthfulness characterizations via convex analysis"):
  with members:
    - "the lovasz bregman divergence and connections to rank aggregation clustering and web ranking"
    - "designing markets for daily deals"
    - "efficiency revenue trade offs in auctions"
    - "approaching utopia strong truthfulness and externality resistant mechanisms"
    - "a new understanding of prediction markets via no regret learning"
    - "statistical exponential families a digest with flash cards"
    - "characterizing truthful multi armed bandit mechanisms"
    - "algorithmic methods for sponsored search advertising"

Connected through hyperedge HE-5 (induced by source paper "a consensual linear opinion pool"):
  with members:
    - "sharing a reward based on peer evaluations"
    - "a truth serum for sharing rewards"

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)