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: "99 revenue via enhanced competition"
  Abstract: A sequence of recent studies show that even in the simple setting of a single seller and a single buyer with additive, independent valuations over $m$ items, the revenue-maximizing mechanism is prohibitively complex. This problem has been addressed using two main approaches: (i) Approximation: the best of two simple mechanisms (sell each item separately, or sell all the items as one bundle) gives $1/6$ of the optimal revenue [BILW14]. (ii) Enhanced competition: running the simple VCG mechanism with additional $m$ buyers extracts at least the optimal revenue in the original market [EFFTW17]. Both approaches, however, suffer from severe drawbacks: On the one hand, losing $83\%$ of the revenue is hardly acceptable in any application. On the other hand, attracting a linear number of new buyers may be prohibitive. Our main result is that by combining the two approaches one can achieve the best of both worlds. Specifically, for any constant $\epsilon$ one can obtain a $(1-\epsilon)$ fraction of the optimal revenue by running simple mechanisms --- either selling each item separately or selling all items as a single bundle --- with substantially fewer additional buyers: logarithmic, constant, or even none in some cases.

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"
    - "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"
    - "learning multi item auctions with or without samples"

Connected through hyperedge HE-2 (induced by source paper "the vickrey auction with a single duplicate bidder approximates the optimal revenue"):
  with members:
    - "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"
    - "vickrey auctions for irregular distributions"

Connected through hyperedge HE-3 (induced by source paper "optimal and benchmark optimal competition complexity for additive buyers over independent items"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "the value of information concealment"
    - "simple mechanisms for subadditive buyers via duality"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "the menu size complexity of revenue approximation"
    - "mechanism design for subadditive agents via an ex ante relaxation"
    - "simple mechanisms for a subadditive buyer and applications to revenue monotonicity"

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)