human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "adversarial learning for revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "learning to bid in revenue maximizing auctions"
    - "learning to clear the market"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a general theory of sample complexity for multi item profit maximization"
    - "revenue optimization with approximate bid predictions"
    - "a field guide to personalized reserve prices"

Connected through hyperedge HE-2 (induced by source paper "incentive aware contextual pricing with non parametric market noise"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "semi parametric dynamic contextual pricing"
    - "nonparametric learning and optimization with covariates"
    - "learning optimal reserve price against non myopic bidders"
    - "contextual search via intrinsic volumes"
    - "ironing in the dark"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

Question: Please tell me which class the center node 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)