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 "differentiable bandit exploration"):
  Abstract: We learn bandit policies that maximize the average reward over bandit instances drawn from an unknown distribution $\mathcal{P}$, from a sample from $\mathcal{P}$. Our approach is an instance of meta-learning and its appeal is that the properties of $\mathcal{P}$ can be exploited without restricting it. We parameterize our policies in a differentiable way and optimize them by policy gradients - an approach that is easy to implement and pleasantly general. Then the challenge is to design effective gradient estimators and good policy classes. To make policy gradients practical, we introduce novel variance reduction techniques. We experiment with various bandit policy classes, including neural networks and a novel soft-elimination policy. The latter has regret guarantees and is a natural starting point for our optimization. Our experiments highlight the versatility of our approach. We also observe that neural network policies can learn implicit biases, which are only expressed through sampled bandit instances during training.

Includes members:
  Member "meta learning of sequential strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards finding longer proofs")
  Member "empirical bayes regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "old dog learns new tricks randomized ucb for bandit problems")
  Member "boltzmann exploration done right":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "making sense of reinforcement learning and probabilistic inference")
  Member "a simple neural attentive meta learner":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting meta learning as supervised learning")
  Member "algorithms for multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "efficient learning in large scale combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in bandits with knapsacks")
  Member "an information theoretic analysis of thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "learning to optimize via posterior sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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)