human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the competition complexity of dynamic mechanism design"):
  Abstract: The Competition Complexity of an auction measures how much competition is needed for the revenue of a simple auction to surpass the optimal revenue. A classic result from auction theory by Bulow and Klemperer [9], states that the Competition Complexity of VCG, in the case of n i.i.d. buyers and a single item, is 1, i.e., it is better to recruit one extra buyer and run a second price auction than to learn exactly the buyers' underlying distribution and run the revenue-maximizing auction tailored to this distribution. #R##N#In this paper we study the Competition Complexity of dynamic auctions. Consider the following setting: a monopolist is auctioning off m items in m consecutive stages to n interested buyers. A buyer realizes her value for item k in the beginning of stage k. We prove that the Competition Complexity of dynamic auctions is at most 3n, and at least linear in n, even when the buyers' values are correlated across stages, under a monotone hazard rate assumption on the stage (marginal) distributions. We also prove results on the number of additional buyers necessary for VCG at every stage to be an {\alpha}-approximation of the optimal revenue; we term this number the {\alpha}-approximate Competition Complexity. As a corollary we provide the first results on prior-independent dynamic auctions. This is, to the best of our knowledge, the first non-trivial positive guarantees for simple ex-post IR dynamic auctions for correlated stages. #R##N#A key step towards proving bounds on the Competition Complexity is getting a good benchmark/upper bound to the optimal revenue. To this end, we extend the recent duality framework of Cai et al. [12] to dynamic settings. As an aside to our approach we obtain a revenue non-monotonicity lemma for dynamic auctions, which may be of independent interest.

Includes members:
  Member "approximating gains from trade in two sided markets via simple mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient two sided markets with limited information")
  Member "approximate revenue maximization with multiple items":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "simple mechanisms for subadditive buyers via duality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bulow klemperer style results for welfare maximization in two sided markets")
  Member "a simple and approximately optimal mechanism for a buyer with complements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a duality based unified approach to bayesian mechanism design")
  Member "optimal dynamic mechanisms with ex post ir via bank accounts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust repeated auctions under heterogeneous buyer behavior")
  Member "a simple and approximately optimal mechanism for an additive buyer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "strong duality for a multiple good monopolist":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a permutation equivariant neural network architecture for auction design")

Question: Please tell me which class the source paper 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)