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 "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"):
  Abstract: For Bayesian combinatorial auctions, we present a general framework for approximately reducing the mechanism design problem for multiple buyers to single buyer sub-problems. Our framework can be applied to any setting which roughly satisfies the following assumptions: (i) buyers' types must be distributed independently (not necessarily identically), (ii) objective function must be linearly separable over the buyers, and (iii) except for the supply constraints, there should be no other inter-buyer constraints. Our framework is general in the sense that it makes no explicit assumption about buyers' valuations, type distributions, and single buyer constraints (e.g., budget, incentive compatibility, etc). #R##N#We present two generic multi buyer mechanisms which use single buyer mechanisms as black boxes; if an $\alpha$-approximate single buyer mechanism can be constructed for each buyer, and if no buyer requires more than $\frac{1}{k}$ of all units of each item, then our generic multi buyer mechanisms are $\gamma_k\alpha$-approximation of the optimal multi buyer mechanism, where $\gamma_k$ is a constant which is at least $1-\frac{1}{\sqrt{k+3}}$. Observe that $\gamma_k$ is at least 1/2 (for $k=1$) and approaches 1 as $k \to \infty$. As a byproduct of our construction, we present a generalization of prophet inequalities. Furthermore, as applications of our framework, we present multi buyer mechanisms with improved approximation factor for several settings from the literature.

Includes members:
  Member "reducing revenue to welfare maximization approximation algorithms and other generalizations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal mechanisms for selling two items to a single buyer having uniformly distributed valuations")
  Member "bayesian optimal auctions via multi to single agent reduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "optimal multi dimensional mechanism design reducing revenue to welfare maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "matroid prophet inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "predict and match prophet inequalities with uncertain supply")
  Member "an algorithmic characterization of multi dimensional mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "extreme value theorems for optimal multidimensional pricing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "escaping cannibalization correlation robust pricing for a unit demand buyer")
  Member "bayesian mechanism design for budget constrained agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a simple mechanism for a budget constrained buyer")
  Member "mechanism design via correlation gap":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prophet inequalities with linear correlations and augmentations")

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)