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 "predict and match prophet inequalities with uncertain supply"):
  Abstract: We consider the problem of selling perishable items to a stream of buyers in order to maximize social welfare. A seller starts with a set of identical items, and each arriving buyer wants any one item, and has a valuation drawn i.i.d. from a known distribution. Each item, however, disappears after an a priori unknown amount of time that we term the horizon for that item. The seller knows the (possibly different) distribution of the horizon for each item, but not its realization till the item actually disappears. As with the classic prophet inequalities, the goal is to design an online pricing scheme that competes with the prophet that knows the horizon and extracts full social surplus (or welfare). #R##N#Our main results are for the setting where items have independent horizon distributions satisfying the monotone-hazard-rate (MHR) condition. Here, for any number of items, we achieve a constant-competitive bound via a conceptually simple policy that balances the rate at which buyers are accepted with the rate at which items are removed from the system. We implement this policy via a novel technique of matching via probabilistically simulating departures of the items at future times. Moreover, for a single item and MHR horizon distribution with mean $\mu$, we show a tight result: There is a fixed pricing scheme that has competitive ratio at most $2 - 1/\mu$, and this is the best achievable in this class. #R##N#We further show that our results are best possible. First, we show that the competitive ratio is unbounded without the MHR assumption even for one item. Further, even when the horizon distributions are i.i.d. MHR and the number of items becomes large, the competitive ratio of any policy is lower bounded by a constant greater than $1$, which is in sharp contrast to the setting with identical deterministic horizons.

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 "prophet secretary surpassing the 1 1 e barrier":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "prophet secretary for combinatorial auctions and matroids":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the power and limits of dynamic pricing in combinatorial markets")
  Member "combinatorial prophet inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "combinatorial auctions via posted prices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "matroid prophet inequalities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequality for bipartite matching merits of being simple and non adaptive")
  Member "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)