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 "pricing to maximize revenue and welfare simultaneously in large markets"):
  Abstract: We study large markets with a single seller which can produce many types of goods, and many multi-minded buyers. The seller chooses posted prices for its many items, and the buyers purchase bundles to maximize their utility. For this setting, we consider the following questions: What fraction of the optimum social welfare does a revenue maximizing solution achieve? Are there pricing mechanisms which achieve both good revenue and good welfare simultaneously? To address these questions, we give efficient pricing schemes which are guaranteed to result in both good revenue and welfare, as long as the buyer valuations for the goods they desire have a nice (although reasonable) structure, e.g., that the aggregate buyer demand has a monotone hazard rate or is not too convex. We also show that our pricing schemes have implications for any pricing which achieves high revenue: specifically that even if the seller cares only about revenue, they can still ensure that their prices result in good social welfare without sacrificing profit. Our results holds for general multi-minded buyers in large markets; we also provide improved guarantees for the important special case of unit-demand buyers.

Includes members:
  Member "do prices coordinate markets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the power and limits of dynamic pricing in combinatorial markets")
  Member "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "envy free pricing in large markets approximating revenue and welfare":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on approximate welfare and revenue maximizing equilibria for size interchangeable bidders")
  Member "applications of alpha strongly regular distributions to bayesian auctions":
    also connected through other hyperedges:
      (none)
  Member "optimising trade offs among stakeholders in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a price per attention auction scheme using mouse cursor information")
  Member "on the ratio of revenue to welfare in single parameter mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applications of alpha strongly regular distributions to bayesian auctions")
  Member "pricing ad slots with consecutive multi unit demand":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating the revenue maximization problem with sharp demands")
  Member "efficiency revenue trade offs in auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "price doubling and item halving robust revenue guarantees for item pricing")

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)