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: "on the optimal sample complexity for best arm identification"
  Abstract: We study the best arm identification (BEST-1-ARM) problem, which is defined as follows. We are given $n$ stochastic bandit arms. The $i$th arm has a reward distribution $D_i$ with an unknown mean $\mu_{i}$. Upon each play of the $i$th arm, we can get a reward, sampled i.i.d. from $D_i$. We would like to identify the arm with the largest mean with probability at least $1-\delta$, using as few samples as possible. We provide a nontrivial algorithm for BEST-1-ARM, which improves upon several prior upper bounds on the same problem. We also study an important special case where there are only two arms, which we call the sign problem. We provide a new lower bound of sign, simplifying and significantly extending a classical result by Farrell in 1964, with a completely new proof. Using the new lower bound for sign, we obtain the first lower bound for BEST-1-ARM that goes beyond the classic Mannor-Tsitsiklis lower bound, by an interesting reduction from Sign to BEST-1-ARM. We propose an interesting conjecture concerning the optimal sample complexity of BEST-1-ARM from the perspective of instance-wise optimality.

Connected through hyperedge HE-1 (induced by source paper "polynomial time algorithms for multiple arm identification with full bandit feedback"):
  with members:
    - "pac identification of many good arms in stochastic multi armed bandits"
    - "nearly optimal sampling algorithms for combinatorial pure exploration"
    - "multiple play bandits in the position based model"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "approximation of the quadratic knapsack problem"
    - "powers of tensors and fast matrix multiplication"
    - "best arm identification in linear bandits"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "polynomial time algorithms for combinatorial pure exploration with full bandit feedback"):
  with members:
    - "pure exploration of multi armed bandit under matroid constraints"
    - "approximation of the quadratic knapsack problem"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "playing against nature causal discovery for decision making under uncertainty"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "towards deep symbolic reinforcement learning"
    - "building machines that learn and think like people"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "taming the monster a fast and simple algorithm for contextual bandits"

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"
    - "towards instance optimal bounds for best arm identification"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "active sequential hypothesis testing"

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

Connected through hyperedge HE-6 (induced by source paper "nearly instance optimal sample complexity bounds for top k arm selection"):
  with members:
    - "towards instance optimal bounds for best arm identification"
    - "pure exploration of multi armed bandit under matroid constraints"
    - "multiple identifications in multi armed bandits"

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

Connected through hyperedge HE-8 (induced by source paper "pure exploration of multi armed bandit under matroid constraints"):
  with members:
    - "multiple identifications in multi armed bandits"
    - "regret in online combinatorial optimization"
    - "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)