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 "online preselection with context information under the plackett luce model"):
  Abstract: We consider an extension of the contextual multi-armed bandit problem, in which, instead of selecting a single alternative (arm), a learner is supposed to make a preselection in the form of a subset of alternatives. More specifically, in each iteration, the learner is presented a set of arms and a context, both described in terms of feature vectors. The task of the learner is to preselect $k$ of these arms, among which a final choice is made in a second step. In our setup, we assume that each arm has a latent (context-dependent) utility, and that feedback on a preselection is produced according to a Plackett-Luce model. We propose the CPPL algorithm, which is inspired by the well-known UCB algorithm, and evaluate this algorithm on synthetic and real data. In particular, we consider an online algorithm selection scenario, which served as a main motivation of our problem setting. Here, an instance (which defines the context) from a certain problem class (such as SAT) can be solved by different algorithms (the arms), but only $k$ of these algorithms can actually be run.

Includes members:
  Member "preselection bandits under the plackett luce model":
    also connected through other hyperedges:
      (none)
  Member "regret minimisation in multinomial logit bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preselection bandits under the plackett luce model")
  Member "multinomial logit bandit with linear utility functions":
    also connected through other hyperedges:
      (none)
  Member "preference based online learning with dueling bandits a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "preselection bandits under the plackett luce model")
  Member "active ranking with subset wise preferences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "best item learning in random utility models with subset choices")
  Member "pac ranking from pairwise and listwise queries lower bounds and upper bounds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "automated algorithm selection survey and perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extreme algorithm selection with dyadic feature representation")
  Member "pac battling bandits in the plackett luce model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "best item learning in random utility models with subset choices")

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)