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 "open problem best arm identification almost instance wise optimality and the gap entropy conjecture"):
  Abstract: The best arm identification problem (BEST-1-ARM) is the most basic pure exploration problem in stochastic multi-armed bandits. The problem has a long history and attracted significant attention for the last decade. However, we do not yet have a complete understanding of the optimal sample complexity of the problem: The state-of-the-art algorithms achieve a sample complexity of $O(\sum_{i=2}^{n} \Delta_{i}^{-2}(\ln\delta^{-1} + \ln\ln\Delta_i^{-1}))$ ($\Delta_{i}$ is the difference between the largest mean and the $i^{th}$ mean), while the best known lower bound is $\Omega(\sum_{i=2}^{n} \Delta_{i}^{-2}\ln\delta^{-1})$ for general instances and $\Omega(\Delta^{-2} \ln\ln \Delta^{-1})$ for the two-arm instances. We propose to study the instance-wise optimality for the BEST-1-ARM problem. Previous work has proved that it is impossible to have an instance optimal algorithm for the 2-arm problem. However, we conjecture that modulo the additive term $\Omega(\Delta_2^{-2} \ln\ln \Delta_2^{-1})$ (which is an upper bound and worst case lower bound for the 2-arm problem), there is an instance optimal algorithm for BEST-1-ARM. Moreover, we introduce a new quantity, called the gap entropy for a best-arm problem instance, and conjecture that it is the instance-wise lower bound. Hence, resolving this conjecture would provide a final answer to the old and basic problem.

Includes members:
  Member "on the optimal sample complexity for best arm identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polynomial time algorithms for multiple arm identification with full bandit feedback")
  Member "instance optimal geometric algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic distribution sensitive point location")
  Member "optimal aggregation algorithms for middleware":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding interesting frames in deep video analytics a top k approach")

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)