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 "pac battling bandits in the plackett luce model"):
  Abstract: We introduce the probably approximately correct (PAC) {Battling-Bandit} problem with the Plackett-Luce (PL) subset choice model---an online learning framework where at each trial the learner chooses a subset of $k$ arms from a fixed set of $n$ arms, and subsequently observes a stochastic feedback indicating preference information of the items in the chosen subset, e.g., the most preferred item or ranking of the top $m$ most preferred items etc. The objective is to identify a near-best item in the underlying PL model {with high confidence}. This generalizes the well-studied PAC {Dueling-Bandit} problem over $n$ arms, which aims to recover the {best-arm} from pairwise preference information, and is known to require $O(\frac{n}{\epsilon^2} \ln \frac{1}{\delta})$ sample complexity. We study the sample complexity of this problem under various feedback models: (1) Winner of the subset (WI), and (2) Ranking of top-$m$ items (TR) for $2\le m \le k$. We show, surprisingly, that with winner information (WI) feedback over subsets of size $2 \leq k \leq n$, the best achievable sample complexity is still $O\left( \frac{n}{\epsilon^2} \ln \frac{1}{\delta}\right)$, independent of $k$, and the same as that in the Dueling Bandit setting ($k=2$). For the more general top-$m$ ranking (TR) feedback model, we show a significantly smaller lower bound on sample complexity of $\Omega\bigg( \frac{n}{m\epsilon^2} \ln \frac{1}{\delta}\bigg)$, which suggests a multiplicative reduction by a factor ${m}$ owing to the additional information revealed from preferences among $m$ items instead of just $1$. We also propose two algorithms for the PAC problem with the TR feedback model with optimal (upto logarithmic factors) sample complexity guarantees, establishing the increase in statistical efficiency from exploiting rank-ordered feedback.

Includes members:
  Member "a nearly instance optimal algorithm for top k ranking under the multinomial logit model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "budget constrained multi armed bandits with multiple plays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained upper confidence reinforcement learning")
  Member "the simulator understanding adaptive sampling in the moderate confidence regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential mode estimation with oracle queries")
  Member "competitive analysis of the top k ranking problem":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "best item learning in random utility models with subset choices")
  Member "data driven rank breaking for efficient rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "relative upper confidence bound for the k armed dueling bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)