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 "reaping the benefits of bundling under high production costs"):
  Abstract: It has long been known in the economics literature that selling different goods in a single bundle can significantly increase revenue, even when the valuations for the goods are independent. However, bundling is no longer profitable if the goods have high production costs. To overcome this issue, we introduce Pure Bundling with Disposal for Cost (PBDC), where after buying the bundle, we allow the customer to return any subset of items for their production cost. We demonstrate using classical examples that PBDC captures the concentration effects of bundling while allowing for the flexibility of individual sales, extracting all of the consumer welfare in situations where previous simple mechanisms could not.Furthermore, we prove a theoretical guarantee on the performance of PBDC that holds for arbitrary independent distributions, using techniques from the mechanism design literature. We transform the problem with costs to a problem with negative valuations, extend the mechanism design techniques to negative valuations, and use the Core-Tail decomposition of Babaioff et al. from [BILW14] to show that either PBDC or individual sales will obtain at least 1/5.2 of the optimal profit. This also improves the bound of 1/6 from [BILW14]. We advance the upper bound as well, constructing two IID items with zero cost where mixed bundling earns 1.19 more revenue than either pure bundling or individual sales.Our numerical experiments show that PBDC outperforms all other simple pricing schemes, including the Bundle-Size Pricing (BSP) introduced by Chu et al. in [CLS11], under the same families of distributions used in [CLS11]. We also provide the first theoretical explanation for some of the great experimental successes in [CLS11]. All in all, our work shows establishes PBDC as a robust, computationally-minimal heuristic that is easy to market to the customer.

Includes members:
  Member "approximate revenue maximization with multiple items":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "on the computational complexity of optimal simple mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "99 revenue via enhanced competition")
  Member "selling two goods optimally":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations")
  Member "simple mechanisms for a subadditive buyer and applications to revenue monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "a simple and approximately optimal mechanism for an additive buyer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "an n to 1 bidder reduction for multi item auctions and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "the menu size complexity of auctions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "simple and nearly optimal multi item auctions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations")

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)