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: "fast core pricing for rich advertising auctions"
  Abstract: As online ad offerings become increasingly complex, with multiple size configurations and layouts available to advertisers, the sale of web advertising space increasingly resembles a combinatorial auction with complementarities. Standard ad auction formats do not immediately extend to these settings, and truthful combinatorial auctions, such as the Vickrey-Clarke-Groves auction, can yield unacceptably low revenue. Core selecting auctions, which apply to combinatorial markets, boost revenue by setting prices so that no group of agents, including the auctioneer, can jointly improve their utilities by switching to a different allocation and payments. Among outcomes in the core, bidder-optimal core points have been the most widely studied due to their incentive properties, such as being implementable at equilibrium. Prior work in economics has studied heuristics for computing approximate bidder-optimal core points given oracle access to the welfare optimization problem, but these solutions either lack performance guarantees or are based on prohibitively slow convex programs. Our main result is a combinatorial algorithm that finds an approximate bidder-optimal core point with almost linear number of calls to the welfare maximization oracle. Our algorithm is faster than previously-proposed heuristics, has theoretical guarantees, and reveals some useful structural properties of the core polytope. We conclude that core pricing is implementable even for very time sensitive practical use cases such as realtime auctions for online advertising and can yield more revenue. We justify this claim experimentally using the Microsoft Bing Ad Auction platform, which allows advertisers to have decorations with a non-uniform number of lines of text. We find that core pricing generates almost 100% more revenue than VCG, and almost 20% more revenue than the standard Generalized Second Price (GSP) auction.

Connected through hyperedge HE-1 (induced by source paper "aiads automated and intelligent advertising system for sponsored search"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "beyond keywords and relevance a personalized ad retrieval framework in e commerce sponsored search"
    - "roi constrained auctions"
    - "optimized cost per click in taobao display advertising"
    - "sponsored search auctions with rich ads"
    - "mechanism design for value maximizers"
    - "scalable semantic matching of queries to ads in sponsored search advertising"
    - "smart pacing for effective online ad campaign optimization"

Connected through hyperedge HE-2 (induced by source paper "the ad types problem"):
  with members:
    - "sponsored search auctions with rich ads"
    - "note on vcg vs price raising for matching markets"
    - "ad auctions and cascade model gsp inefficiency and algorithms"
    - "ironing in the dark"
    - "sponsored search market equilibria and the hungarian method"
    - "sponsored search auctions with markovian users"

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)