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 "advances in bandits with knapsacks"):
  Abstract: Bandits with Knapsacks (\BwK) is a general model for multi-armed bandits under supply/budget constraints. While worst-case regret bounds for \BwK are well-understood, we focus on logarithmic instance-dependent regret bounds. We largely resolve them for one limited resource other than time, and for known, deterministic resource consumption. We also bound regret within a given round ("simple regret"). One crucial technique analyzes the sum of the confidence terms of the chosen arms. This technique allows to import the insights from prior work on bandits without resources, which leads to several extensions.

Includes members:
  Member "introduction to multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "unifying the stochastic and the adversarial bandits with knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial bandits with knapsacks")
  Member "adversarial bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial attacks on linear contextual bandits")
  Member "combinatorial semi bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "logarithmic regret bounds for bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a farewell to arms sequential reward maximization on a budget with a giving up option")
  Member "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")
  Member "efficient learning in large scale combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable bandit exploration")
  Member "resourceful contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "budget constrained bandits over general cost and reward distributions")

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)