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 "quantifying the burden of exploration and the unfairness of free riding"):
  Abstract: We consider the multi-armed bandit setting with a twist. Rather than having just one decision maker deciding which arm to pull in each round, we have $n$ different decision makers (agents). In the simple stochastic setting we show that one of the agents (called the free rider), who has access to the history of other agents playing some zero-regret algorithm can achieve just $O(1)$ regret, as opposed to the regret lower bound of $\Omega (\log T)$ when one decision maker is playing in isolation. In the linear contextual setting, we show that if the other agents play a particular, popular zero-regret algorithm (UCB), then the free rider can again achieve $O(1)$ regret. In order to prove this result, we give a deterministic lower bound on the number of times each suboptimal arm must be pulled in UCB. In contrast, we show that the free-rider cannot beat the standard single-player regret bounds in certain partial information settings. Lastly, we complement our theoretical results with simulations for both stochastic and contextual settings.

Includes members:
  Member "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "the externalities of exploration and how data diversity helps exploitation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "lean from thy neighbor stochastic adversarial bandits in a network":
    also connected through other hyperedges:
      (none)
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi source domain adaptation for text classification via distancenet 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)