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 "lp based approximation for personalized reserve prices"):
  Abstract: We study the problem of computing personalized reserve prices in eager second price auctions without having any assumption on valuation distributions. Here, the input is a dataset that contains the submitted bids of $n$ buyers in a set of auctions and the goal is to return personalized reserve prices $\textbf r$ that maximize the revenue earned on these auctions by running eager second price auctions with reserve $\textbf r$. We present a novel LP formulation to this problem and a rounding procedure which achieves a $(1+2(\sqrt{2}-1)e^{\sqrt{2}-2})^{-1} \approx 0.684$-approximation. This improves over the $\frac{1}{2}$-approximation Algorithm due to Roughgarden and Wang. We show that our analysis is tight for this rounding procedure. We also bound the integrality gap of the LP, which bounds the performance of any algorithm based on this LP.

Includes members:
  Member "prophet secretary through blind strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "improved approximations for free order prophets and second price auctions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assortment auctions a myersonian characterization for markov chain based choice models")
  Member "prophet secretary surpassing the 1 1 e barrier":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "a field guide to personalized reserve prices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "making the most of your samples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "learning theory and algorithms for revenue optimization in second price auctions with reserve":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "mechanism design via correlation gap":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "optimal auctions with correlated bidders are easy":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "combinatorial auctions with interdependent valuations sos to the rescue")

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)