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 "prophets secretaries and maximizing the probability of choosing the best"):
  Abstract: Suppose a customer is faced with a sequence of fluctuating prices, such as for airfare or a product sold by a large online retailer. Given distributional information about what price they might face each day, how should they choose when to purchase in order to maximize the likelihood of getting the best price in retrospect? This is related to the classical secretary problem, but with values drawn from known distributions. In their pioneering work, Gilbert and Mosteller [\textit{J. Amer. Statist. Assoc. 1966}] showed that when the values are drawn i.i.d., there is a thresholding algorithm that selects the best value with probability approximately $0.5801$. However, the more general problem with non-identical distributions has remained unsolved. #R##N#In this paper we provide an algorithm for the case of non-identical distributions that selects the maximum element with probability $1/e$, and we show that this is tight. We further show that if the observations arrive in a random order, this barrier of $1/e$ can be broken using a static threshold algorithm, and we show that our success probability is the best possible for any single-threshold algorithm under random observation order. Moreover, we prove that one can achieve a strictly better success probability using more general multi-threshold algorithms, unlike the non-random-order case. Along the way, we show that the best achievable success probability for the random-order case matches that of the i.i.d.\ case, which is approximately $0.5801$, under a "no-superstars" condition that no single distribution is very likely ex ante to generate the maximum value. We also extend our results to the problem of selecting one of the $k$ best values.

Includes members:
  Member "stochastic k server how should uber work":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "putting ridesharing to the test efficient and scalable solutions and the power of dynamic vehicle relocation")
  Member "prophet secretary for combinatorial auctions and matroids":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "online matroid intersection beating half for random arrival":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved truthful mechanisms for combinatorial auctions with submodular bidders")
  Member "o log log rank competitive ratio for the matroid secretary problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "submodular matroid secretary problem with shortlists")
  Member "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "submodular matroid secretary problem with shortlists")
  Member "online independent set beyond the worst case secretaries prophets and periods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust algorithms for the secretary problem")
  Member "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-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.DS(Data Structures and Algorithms)