human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "ranked bandits in metric spaces learning optimally diverse rankings over large document collections"
  Abstract: Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-to-rank formulation that optimizes the fraction of satisfied users, with several scalable algorithms that explicitly takes document similarity and ranking context into account. Our formulation is a non-trivial common generalization of two multi-armed bandit models from the literature: "ranked bandits" (Radlinski et al., ICML 2008) and "Lipschitz bandits" (Kleinberg et al., STOC 2008). We present theoretical justifications for this approach, as well as a near-optimal algorithm. Our evaluation adds optimizations that improve empirical performance, and shows that our algorithms learn orders of magnitude more quickly than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"):
  with members:
    - "contextual bandits with global constraints and objective"
    - "incentivizing high quality crowdwork"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-2 (induced by source paper "bandits and experts in metric spaces"):
  with members:
    - "bandits with concave rewards and convex knapsacks"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "parallelizing exploration exploitation tradeoffs with gaussian process bandit optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "truthful mechanisms with implicit payment computation"

Question: Please tell me which class the center node 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)