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: "selecting weight functions to spur competition in quasi proportional auctions"
  Abstract: In quasi-proportional auctions, each bidder receives a fraction of the allocation equal to the weight of his bid divided by the sum of the weights of all bids, where a bid's weight is determined by a chosen weight function. We study the relationship between the weight function, bidders' private values, and the auctioneer's revenue in equilibrium. #R##N#It has been shown that if one bidder has a much higher private value than the others, then the revenue-optimizing weight function is nearly flat. Essentially, threatening the bidder with the highest valuation to share the allocation of the rewards maximizes the auctioneer's revenue. We show that as bidder private values approach parity, the revenue-maximizing weight function becomes steeper, making the quasi-proportional auction more like a winner-take-all auction. #R##N#For flatter weight functions, there is known to be a unique pure-strategy Nash equilibrium. We show that this also holds for steeper weight functions, at least in the two-bidder case. We also give some analysis and intuition about why this may hold for more bidders.

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)