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 "on the computational complexity of optimal simple mechanisms"):
  Abstract: We consider a monopolist seller facing a single buyer with additive valuations over n heterogeneous, independent items. It is known that in this important setting optimal mechanisms may require randomization [HR12], use menus of infinite size [DDT15], and may be computationally intractable [DDT14]. This has sparked recent interest in finding simple mechanisms that obtain reasonable approximations to the optimal revenue [HN12, LY13, BILW14]. In this work we attempt to find the optimal simple mechanism. #R##N#There are many ways to define simple mechanisms. Here we restrict our search to partition mechanisms, where the seller partitions the items into disjoint bundles and posts a price for each bundle; the buyer is allowed to buy any number of bundles. #R##N#We give a PTAS for the problem of finding a revenue-maximizing partition mechanism, and prove that the problem is strongly NP-hard. En route, we prove structural properties of near-optimal partition mechanisms which may be of independent interest: for example, there always exists a near-optimal partition mechanism that uses only a constant number of non-trivial bundles (i.e. bundles with more than one item).

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 "the pseudo dimension of near optimal auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "settling the sample complexity of single parameter revenue maximization")
  Member "simple mechanisms for a subadditive buyer and applications to revenue monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "strong duality for a multiple good monopolist":
    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 "revenue maximization for selling multiple correlated items":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "the complexity of optimal multidimensional pricing":
    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)