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: "vickrey auctions for irregular distributions"
  Abstract: The classic result of Bulow and Klemperer \cite{BK96} says that in a single-item auction recruiting one more bidder and running the Vickrey auction achieves a higher revenue than the optimal auction's revenue on the original set of bidders, when values are drawn i.i.d. from a regular distribution. We give a version of Bulow and Klemperer's result in settings where bidders' values are drawn from non-i.i.d. irregular distributions. We do this by modeling irregular distributions as some convex combination of regular distributions. The regular distributions that constitute the irregular distribution correspond to different population groups in the bidder population. Drawing a bidder from this collection of population groups is equivalent to drawing from some convex combination of these regular distributions. We show that recruiting one extra bidder from each underlying population group and running the Vickrey auction gives at least half of the optimal auction's revenue on the original set of bidders.

Connected through hyperedge HE-1 (induced by source paper "bulow klemperer style results for welfare maximization in two sided markets"):
  with members:
    - "the vickrey auction with a single duplicate bidder approximates the optimal revenue"
    - "99 revenue via enhanced competition"
    - "the best of both worlds asymptotically efficient mechanisms with a guarantee on the expected gains from trade"
    - "are two samples really better than one on the non asymptotic performance of empirical revenue maximization"
    - "optimal and benchmark optimal competition complexity for additive buyers over independent items"
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "submultiplicative glivenko cantelli and uniform convergence of revenues"
    - "a general theory of sample complexity for multi item profit maximization"

Connected through hyperedge HE-2 (induced by source paper "the vickrey auction with a single duplicate bidder approximates the optimal revenue"):
  with members:
    - "99 revenue via enhanced competition"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "randomization beats second price as a prior independent auction"
    - "optimal auctions vs anonymous pricing"
    - "prior independent mechanisms for scheduling"

Connected through hyperedge HE-3 (induced by source paper "optimal multi unit mechanisms with private demands"):
  with members:
    - "approximate revenue maximization with multiple items"
    - "simple mechanisms for subadditive buyers via duality"
    - "kernel based methods for bandit convex optimization"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"
    - "duality and optimality of auctions for uniform distributions"
    - "a simple and approximately optimal mechanism for an additive buyer"

Connected through hyperedge HE-4 (induced by source paper "the value of information concealment"):
  with members:
    - "optimal multi unit mechanisms with private demands"
    - "optimal auctions for negatively correlated items"
    - "a simple and approximately optimal mechanism for an additive buyer"
    - "an n to 1 bidder reduction for multi item auctions and its applications"
    - "optimal deterministic auctions with correlated priors"

Connected through hyperedge HE-5 (induced by source paper "ironing in the dark"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the pseudo dimension of near optimal auctions"
    - "optimal auctions for correlated buyers with sampling"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "mechanism design for data science"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "envy freedom and prior free mechanism design"

Connected through hyperedge HE-6 (induced by source paper "the sample complexity of auctions with side information"):
  with members:
    - "the sample complexity of revenue maximization"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "ranking and tradeoffs in sponsored search auctions"

Connected through hyperedge HE-7 (induced by source paper "making the most of your samples"):
  with members:
    - "the sample complexity of revenue maximization"
    - "optimal auctions for correlated buyers with sampling"
    - "optimal platform design"
    - "mechanism design for data science"

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)