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 "best item learning in random utility models with subset choices"):
  Abstract: We consider the problem of PAC learning the most valuable item from a pool of $n$ items using sequential, adaptively chosen plays of subsets of $k$ items, when, upon playing a subset, the learner receives relative feedback sampled according to a general Random Utility Model (RUM) with independent noise perturbations to the latent item utilities. We identify a new property of such a RUM, termed the minimum advantage, that helps in characterizing the complexity of separating pairs of items based on their relative win/loss empirical counts, and can be bounded as a function of the noise distribution alone. We give a learning algorithm for general RUMs, based on pairwise relative counts of items and hierarchical elimination, along with a new PAC sample complexity guarantee of $O(\frac{n}{c^2\epsilon^2} \log \frac{k}{\delta})$ rounds to identify an $\epsilon$-optimal item with confidence $1-\delta$, when the worst case pairwise advantage in the RUM has sensitivity at least $c$ to the parameter gaps of items. Fundamental lower bounds on PAC sample complexity show that this is near-optimal in terms of its dependence on $n,k$ and $c$.

Includes members:
  Member "active ranking with subset wise preferences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "pac ranking from pairwise and listwise queries lower bounds and upper bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "pac battling bandits in the plackett luce model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (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-5 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "competitive analysis of the top k ranking problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sorted top k in rounds")
  Member "data driven rank breaking for efficient rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "relative upper confidence bound for the k armed dueling bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regret minimization in stochastic contextual dueling 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)