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 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.

Connected through hyperedge HE-1 (induced by source paper "persuasion and incentives through the lens of duality"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "algorithmic persuasion with no externalities"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "computational aspects of private bayesian persuasion"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-2 (induced by source paper "simple and approximately optimal pricing for proportional complementarities"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "reaping the benefits of bundling under high production costs"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-3 (induced by source paper "99 revenue via enhanced competition"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "on revenue monotonicity in combinatorial auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "reaping the benefits of bundling under high production costs"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "on the computational complexity of optimal simple mechanisms"

Connected through hyperedge HE-4 (induced by source paper "selling to a no regret buyer"):
  with members:
    - "learning multi item auctions with or without samples"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sequential mechanisms with ex post participation guarantees"
    - "optimal dynamic mechanisms with ex post ir via bank accounts"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "the pseudo dimension of near optimal 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)