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 "competing bandits in matching markets"):
  Abstract: Stable matching, a classical model for two-sided markets, has long been studied with little consideration for how each side's preferences are learned. With the advent of massive online markets powered by data-driven matching platforms, it has become necessary to better understand the interplay between learning and market objectives. We propose a statistical learning model in which one side of the market does not have a priori knowledge about its preferences for the other side and is required to learn these from stochastic rewards. Our model extends the standard multi-armed bandits framework to multiple players, with the added feature that arms have preferences over players. We study both centralized and decentralized approaches to this problem and show surprising exploration-exploitation trade-offs compared to the single player multi-armed bandits setting.

Includes members:
  Member "non stochastic multi player multi armed bandits optimal rate with collision information sublinear without":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "competing bandits the perils of exploration under competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandits with adversarial scaling")
  Member "multiplayer bandits without observing collision information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "selfish robustness and equilibria in multi player bandits")
  Member "competing bandits learning under competition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "delay and cooperation in nonstochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the gossiping insert eliminate algorithm for multi agent bandits")
  Member "matching while learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal dynamic information provision in traffic routing")
  Member "multi player bandits a musical chairs approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")
  Member "concurrent bandits and cognitive radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "my fair bandit distributed learning of max min fairness with multi player bandits")

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.LG(Machine Learning)