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: "on nash dynamics of matching market equilibria"
  Abstract: In this paper, we study the Nash dynamics of strategic interplays of n buyers in a matching market setup by a seller, the market maker. Taking the standard market equilibrium approach, upon receiving submitted bid vectors from the buyers, the market maker will decide on a price vector to clear the market in such a way that each buyer is allocated an item for which he desires the most (a.k.a., a market equilibrium solution). While such equilibrium outcomes are not unique, the market maker chooses one (maxeq) that optimizes its own objective --- revenue maximization. The buyers in turn change bids to their best interests in order to obtain higher utilities in the next round's market equilibrium solution. #R##N#This is an (n+1)-person game where buyers place strategic bids to gain the most from the market maker's equilibrium mechanism. The incentives of buyers in deciding their bids and the market maker's choice of using the maxeq mechanism create a wave of Nash dynamics involved in the market. We characterize Nash equilibria in the dynamics in terms of the relationship between maxeq and mineq (i.e., minimum revenue equilibrium), and develop convergence results for Nash dynamics from the maxeq policy to a mineq solution, resulting an outcome equivalent to the truthful VCG mechanism. #R##N#Our results imply revenue equivalence between maxeq and mineq, and address the question that why short-term revenue maximization is a poor long run strategy, in a deterministic and dynamic setting.

Connected through hyperedge HE-1 (induced by source paper "proportional dynamics in exchange economies"):
  with members:
    - "an alternating algorithm for finding linear arrow debreu market equilibrium"
    - "universal growth in production economies"
    - "a strongly polynomial algorithm for linear exchange markets"
    - "tracing equilibrium in dynamic markets via distributed adaptation"
    - "dynamics of distributed updating in fisher markets"
    - "selling complementary goods dynamics efficiency and revenue"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "the price of anarchy in auctions"

Connected through hyperedge HE-2 (induced by source paper "universal growth in production economies"):
  with members:
    - "selling complementary goods dynamics efficiency and revenue"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "nash social welfare approximation for strategic agents"
    - "the price of anarchy in auctions"
    - "learning and efficiency in games with dynamic population"
    - "learning in auctions regret is hard envy is easy"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"
    - "concave generalized flows with applications to market equilibria"

Connected through hyperedge HE-3 (induced by source paper "walrasian dynamics in multi unit markets"):
  with members:
    - "optimal multi unit mechanisms with private demands"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "when does the price of anarchy tend to 1 in large walrasian auctions and fisher markets"
    - "the menu size complexity of auctions"
    - "non price equilibria in markets of discrete goods"
    - "multi unit auctions beyond roberts"
    - "price of anarchy for greedy 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)