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: "nearly instance optimal sample complexity bounds for top k arm selection"
  Abstract: In the Best-$k$-Arm problem, we are given $n$ stochastic bandit arms, each associated with an unknown reward distribution. We are required to identify the $k$ arms with the largest means by taking as few samples as possible. In this paper, we make progress towards a complete characterization of the instance-wise sample complexity bounds for the Best-$k$-Arm problem. On the lower bound side, we obtain a novel complexity term to measure the sample complexity that every Best-$k$-Arm instance requires. This is derived by an interesting and nontrivial reduction from the Best-$1$-Arm problem. We also provide an elimination-based algorithm that matches the instance-wise lower bound within doubly-logarithmic factors. The sample complexity of our algorithm strictly dominates the state-of-the-art for Best-$k$-Arm (module constant factors).

Connected through hyperedge HE-1 (induced by source paper "tight lower bounds for combinatorial multi armed bandits"):
  with members:
    - "top k combinatorial bandits with full bandit feedback"
    - "thompson sampling for combinatorial network optimization in unknown environments"
    - "thompson sampling for combinatorial semi bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial semi bandit with known covariance"
    - "combinatorial cascading bandits"
    - "combinatorial bandits revisited"

Connected through hyperedge HE-2 (induced by source paper "exploratory machine learning with unknown unknowns"):
  with members:
    - "an unbiased risk estimator for learning with augmented classes"
    - "fast classification rates without standard margin assumptions"
    - "active feature acquisition with supervised matrix completion"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"

Connected through hyperedge HE-3 (induced by source paper "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "skyline computation with noisy comparisons"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "instance optimal geometric algorithms"
    - "optimal adaptive algorithms for finding the nearest and farthest point on a parametric black box curve"

Connected through hyperedge HE-4 (induced by source paper "approximate ranking from pairwise comparisons"):
  with members:
    - "the simulator understanding adaptive sampling in the moderate confidence regime"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "tuned models of peer assessment in moocs"

Connected through hyperedge HE-5 (induced by source paper "nearly optimal sampling algorithms for combinatorial pure exploration"):
  with members:
    - "towards instance optimal bounds for best arm identification"
    - "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-6 (induced by source paper "the simulator understanding adaptive sampling in the moderate confidence regime"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "matroid bandits fast combinatorial optimization with learning"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-7 (induced by source paper "towards instance optimal bounds for best arm identification"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"
    - "on the optimal sample complexity for best arm identification"
    - "instance optimal geometric algorithms"
    - "multiple identifications in multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)