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 "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.

Includes members:
  Member "a duality based unified approach to bayesian mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  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 "on the competition complexity of dynamic mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "persuasion and incentives through the lens of duality")
  Member "on revenue monotonicity in combinatorial auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "settling the sample complexity of single parameter revenue maximization")
  Member "simple mechanisms for subadditive buyers via duality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "bulow klemperer style results for welfare maximization in two sided markets")
  Member "reaping the benefits of bundling under high production costs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "simple and approximately optimal pricing for proportional complementarities")
  Member "mechanism design for subadditive agents via an ex ante relaxation":
    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)