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 "envy regret and social welfare loss"):
  Abstract: Incentive compatibility (IC) is one of the most fundamental properties of an auction mechanism, including those used for online advertising. Recent methods by Feng et al. and Lahaie et al. show that counterfactual runs of the auction mechanism with different bids can be used to determine whether an auction is IC. In this paper we show that a similar result can be obtained by looking at the advertisers' envy, which can be computed with one single execution of the auction. We introduce two metrics to evaluate the incentive-compatibility of an auction: IC-Regret and IC-Envy. For position auction environments, we show that for a large class of pricing schemes (which includes e.g. VCG and GSP), IC-Envy $\ge$ IC-Regret (and IC-Envy = IC-Regret when bids are distinct). We consider non-separable discounts in the Ad Types environment of Colini-Baldeschi et al. where we show that for a generalization of GSP also IC-Envy $\ge$ IC-Regret. Our final theoretical result is that in all these settings IC-Envy be used to bound the loss in social welfare due advertiser misreports. #R##N#Finally, we show that IC-Envy is useful as a feature to predict IC-Regret in auction environments beyond the ones for which we show theoretical results. In particular, using IC-Envy yields better results than training models using only price and value features.

Includes members:
  Member "the ad types problem":
    also connected through other hyperedges:
      (none)
  Member "online learning for measuring incentive compatibility in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the intrinsic robustness of stochastic bandits to strategic manipulation")
  Member "note on vcg vs price raising for matching markets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the ad types problem")
  Member "communication complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "the price of anarchy in auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "setting lower bounds on truthfulness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the nisan ronen conjecture for submodular valuations")
  Member "learning in auctions regret is hard envy is easy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning reserve prices in second price auctions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)