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: "simplicity expressiveness tradeoffs in mechanism design"
  Abstract: A fundamental result in mechanism design theory, the so-called revelation principle, asserts that for many questions concerning the existence of mechanisms with a given outcome one can restrict attention to truthful direct revelation-mechanisms. In practice, however, many mechanism use a restricted message space. This motivates the study of the tradeoffs involved in choosing simplified mechanisms, which can sometimes bring benefits in precluding bad or promoting good equilibria, and other times impose costs on welfare and revenue. We study the simplicity-expressiveness tradeoff in two representative settings, sponsored search auctions and combinatorial auctions, each being a canonical example for complete information and incomplete information analysis, respectively. We observe that the amount of information available to the agents plays an important role for the tradeoff between simplicity and expressiveness.

Connected through hyperedge HE-1 (induced by source paper "interpolating between truthful and non truthful mechanisms for combinatorial auctions"):
  with members:
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "inapproximability of truthful mechanisms via generalizations of the vc dimension"
    - "clinching auctions beyond hard budget constraints"
    - "price of anarchy for auction revenue"
    - "on the efficiency of the walrasian mechanism"
    - "expressiveness and robustness of first price position auctions"
    - "the computational complexity of truthfulness in combinatorial auctions"
    - "bayesian sequential auctions"

Connected through hyperedge HE-2 (induced by source paper "efficient advert assignment"):
  with members:
    - "econometrics for learning agents"
    - "optimising trade offs among stakeholders in ad auctions"
    - "a dynamic axiomatic approach to first price auctions"
    - "bayesian sequential auctions"
    - "online advertisement optimization and stochastic networks"
    - "truthful mechanisms with implicit payment computation"
    - "bidding to the top vcg and equilibria of position based auctions"

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)