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: "towards instance optimal bounds for best arm identification"
  Abstract: In the classical best arm identification (Best-$1$-Arm) problem, we are given $n$ stochastic bandit arms, each associated with a reward distribution with an unknown mean. We would like to identify the arm with the largest mean with probability at least $1-\delta$, using as few samples as possible. Understanding the sample complexity of Best-$1$-Arm has attracted significant attention since the last decade. However, the exact sample complexity of the problem is still unknown. #R##N#Recently, Chen and Li made the gap-entropy conjecture concerning the instance sample complexity of Best-$1$-Arm. Given an instance $I$, let $\mu_{[i]}$ be the $i$th largest mean and $\Delta_{[i]}=\mu_{[1]}-\mu_{[i]}$ be the corresponding gap. $H(I)=\sum_{i=2}^n\Delta_{[i]}^{-2}$ is the complexity of the instance. The gap-entropy conjecture states that $\Omega\left(H(I)\cdot\left(\ln\delta^{-1}+\mathsf{Ent}(I)\right)\right)$ is an instance lower bound, where $\mathsf{Ent}(I)$ is an entropy-like term determined by the gaps, and there is a $\delta$-correct algorithm for Best-$1$-Arm with sample complexity $O\left(H(I)\cdot\left(\ln\delta^{-1}+\mathsf{Ent}(I)\right)+\Delta_{[2]}^{-2}\ln\ln\Delta_{[2]}^{-1}\right)$. If the conjecture is true, we would have a complete understanding of the instance-wise sample complexity of Best-$1$-Arm. #R##N#We make significant progress towards the resolution of the gap-entropy conjecture. For the upper bound, we provide a highly nontrivial algorithm which requires \[O\left(H(I)\cdot\left(\ln\delta^{-1} +\mathsf{Ent}(I)\right)+\Delta_{[2]}^{-2}\ln\ln\Delta_{[2]}^{-1}\mathrm{polylog}(n,\delta^{-1})\right)\] samples in expectation. For the lower bound, we show that for any Gaussian Best-$1$-Arm instance with gaps of the form $2^{-k}$, any $\delta$-correct monotone algorithm requires $\Omega\left(H(I)\cdot\left(\ln\delta^{-1} + \mathsf{Ent}(I)\right)\right)$ samples in expectation.

Connected through hyperedge HE-1 (induced by source paper "collaborative learning with limited interaction tight bounds for distributed exploration in multi armed bandits"):
  with members:
    - "improved algorithms for collaborative pac learning"
    - "tight bounds for collaborative pac learning via multiplicative weights"
    - "adaptive multiple arm identification"
    - "delay and cooperation in nonstochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "distributed exploration in multi armed bandits"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "thresholding bandit with optimal aggregate regret"):
  with members:
    - "statistical decision making for optimal budget allocation in crowd labeling"

Connected through hyperedge HE-3 (induced by source paper "learning the distribution with largest mean two bandit frameworks"):
  with members:
    - "learning to detect an oddball target"
    - "unimodal bandits without smoothness"
    - "further optimal regret bounds for thompson sampling"
    - "optimal discovery with probabilistic expert advice finite time analysis and macroscopic optimality"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-4 (induced by source paper "nearly optimal sampling algorithms for combinatorial pure exploration"):
  with members:
    - "nearly instance optimal sample complexity bounds for top k arm selection"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "active sequential hypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "nearly instance optimal sample complexity bounds for top k arm selection"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-6 (induced by source paper "adaptive multiple arm identification"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"

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)