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: "competitive auctions for markets with positive externalities"
  Abstract: In digital goods auctions, there is an auctioneer who sells an item with unlimited supply to a set of potential buyers, and the objective is to design truthful auction to maximize the total profit of the auctioneer. Motivated from an observation that the values of buyers for the item could be interconnected through social networks, we study digital goods auctions with positive externalities among the buyers. This defines a multi-parameter auction design problem where the private valuation of every buyer is a function of other winning buyers. The main contribution of this paper is a truthful competitive mechanism for subadditive valuations. Our competitive result is with respect to a new solution benchmark $\mathcal{F}^{(3)}$; on the other hand, we show a surprising impossibility result if comparing to the benchmark $\mathcal{F}^{(2)}$, where the latter has been used quite successfully in digital goods auctions without extenalities \cite{Goldberg2006}. Our results from $\mathcal{F}^{(2)}$ to $\mathcal{F}^{(3)}$ could be considered as the loss of optimal profit at the cost of externalities.

Connected through hyperedge HE-1 (induced by source paper "improved efficiency guarantees in auctions with budgets"):
  with members:
    - "efficiency guarantees in auctions with budgets"
    - "budget feasible mechanism design from prior free to bayesian"
    - "prior free auctions for budgeted agents"
    - "composable and efficient mechanisms"
    - "bayesian mechanism design for budget constrained agents"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

Connected through hyperedge HE-2 (induced by source paper "competitive analysis via benchmark decomposition"):
  with members:
    - "optimal competitive auctions"
    - "on random sampling auctions for digital goods"
    - "the biased sampling profit extraction auction"
    - "prior free auctions for budgeted agents"
    - "envy freedom and prior free mechanism design"
    - "mechanism design via consensus estimates cross checking and profit extraction"

Connected through hyperedge HE-3 (induced by source paper "optimal competitive auctions"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "on random sampling auctions for digital goods"
    - "optimal mechansim design and money burning"

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)