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 "estimating approximate incentive compatibility"):
  Abstract: In practice, most mechanisms for selling, buying, matching, voting, and so on are not incentive compatible. We present techniques for estimating how far a mechanism is from incentive compatible. Given samples from the agents' type distribution, we show how to estimate the extent to which an agent can improve his utility by misreporting his type. We do so by first measuring the maximum utility an agent can gain by misreporting his type on average over the samples, assuming his true and reported types are from a finite subset -- which our technique constructs -- of the type space. The challenge is that by measuring utility gains over a finite subset of the type space, we might miss pairs of types $\theta$ and $\hat{\theta}$ where an agent with type $\theta$ can greatly improve his utility by reporting the type $\hat{\theta}$. Our technique discretizes the type space by constructing a learning-theoretic cover in a higher-dimensional space. The key technical contribution is proving that the maximum utility gain over this finite subset nearly matches the maximum utility gain overall, despite the volatility of the utility functions we study. We apply our tools to the single-item and combinatorial first-price auctions, generalized second-price auction, discriminatory auction, uniform-price auction, and second-price auction with spiteful bidders. To our knowledge, these are the first guarantees for estimating approximate incentive compatibility from the mechanism designer's perspective.

Includes members:
  Member "a duality based unified approach to bayesian mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "approximate revenue maximization with multiple items":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "optimal auctions through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bittensor an intermodel intelligence measure")
  Member "dispersion for data driven algorithm design online learning and private optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning piecewise lipschitz functions in changing environments")
  Member "a general theory of sample complexity for multi item profit maximization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limitations of incentive compatibility on discrete type spaces")
  Member "learning multi item auctions with or without samples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simple credible and approximately optimal auctions")
  Member "a b testing of auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online inference for advertising auctions")
  Member "learning simple auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

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)