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 "exponential weight functions for quasi proportional auctions"):
  Abstract: AbstractIn quasi-proportional auctions, the allocation is shared among bid-ders in proportion to their weighted bids. The auctioneer selects a bidweight function, and bidders know the weight function when they bid.In this note, we analyze how weight functions that are exponential inthe bid a ect bidder behavior. We show that exponential weight func-tions have a pure-strategy Nash equilibrium, we characterize bids atan equilibrium, and we compare it to an equilibrium for power weightfunctions. 1 Introduction Quasi-proportional auctions [2, 9] award each bidder a share of the allocationproportional to their weighted bid. Speci cally, if b is the vector of bids, b i is bidder i’s bid, and fis the weight function, then bidder ihas allocation:a i (b) =f(b i )P j f(b j ): (1)We focus on winners-pay, so each bidder pays their bid times their allocation.Let v i be bidder i’s private value for a full allocation and assume linearvaluation. Then bidder ihas utility (pro t):u i (b) = (v i b i )a i

Includes members:
  Member "the sample complexity of revenue maximization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "sampling and representation 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")

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)