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 "preselection bandits under the plackett luce model"):
  Abstract: In this paper, we introduce the Preselection Bandit problem, in which the learner preselects a subset of arms (choice alternatives) for a user, which then chooses the final arm from this subset. The learner is not aware of the user's preferences, but can learn them from observed choices. In our concrete setting, we allow these choices to be stochastic and model the user's actions by means of the Plackett-Luce model. The learner's main task is to preselect subsets that eventually lead to highly preferred choices. To formalize this goal, we introduce a reasonable notion of regret and derive lower bounds on the expected regret. Moreover, we propose algorithms for which the upper bound on expected regret matches the lower bound up to a logarithmic term of the time horizon.

Includes members:
  Member "combinatorial bandits with relative feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "regret minimisation in multinomial logit bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "preference based online learning with dueling bandits a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "automated algorithm selection survey and perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "pac battling bandits in the plackett luce model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "a nearly instance optimal algorithm for top k ranking under the multinomial logit model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "multi dueling bandits with dependent arms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "thompson sampling for the mnl bandit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "delay adaptive learning in generalized linear contextual 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)