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: "from bayesian to crowdsourced bayesian auctions"
  Abstract: A strong assumption in Bayesian mechanism design is that the distributions of the players' private types are common knowledge to the designer and the players--the common prior assumption. An important problem that has received a lot of attention in both economics and computer science is to repeatedly weaken this assumption in game theory--the "Wilson's Doctrine". In this work we consider, for the first time in the literature, multi-item auctions where the knowledge about the players' value distributions is scattered among the players and the seller. Each one of them privately knows some or none of the value distributions, no constraint is imposed on who knows which distributions, and the seller does not know who knows what. In such an unstructured information setting, we design mechanisms for unit-demand and additive auctions, whose expected revenue approximates that of the optimal Bayesian mechanisms by "crowdsourcing" the players' and the seller's knowledge. Our mechanisms are 2-step dominant-strategy truthful and the revenue increases gracefully with the amount of knowledge the players have. In particular, the revenue starts from a constant fraction of the revenue of the best known dominant-strategy truthful Bayesian mechanisms, and approaches 100 percent of the later when the amount of knowledge increases. Our results greatly improve the literature on the relationship between the amount of knowledge in the system and what mechanism design can achieve. In some sense, our results show that the common prior assumption is without much loss of generality in Bayesian auctions if one is willing to give up a fraction of the revenue.

Connected through hyperedge HE-1 (induced by source paper "bayesian auctions with efficient queries"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "learning multi item auctions with or without samples"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

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)