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: "time sensitive bandit learning and satisficing thompson sampling"
  Abstract: The literature on bandit learning and regret analysis has focused on contexts where the goal is to converge on an optimal action in a manner that limits exploration costs. One shortcoming imposed by this orientation is that it does not treat time preference in a coherent manner. Time preference plays an important role when the optimal action is costly to learn relative to near-optimal actions. This limitation has not only restricted the relevance of theoretical results but has also influenced the design of algorithms. Indeed, popular approaches such as Thompson sampling and UCB can fare poorly in such situations. In this paper, we consider discounted rather than cumulative regret, where a discount factor encodes time preference. We propose satisficing Thompson sampling -- a variation of Thompson sampling -- and establish a strong discounted regret bound for this new algorithm.

Connected through hyperedge HE-1 (induced by source paper "long term visitation value for deep exploration in sparse reward reinforcement learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "exploration by random network distillation"
    - "is q learning provably efficient"
    - "parameter space noise for exploration"
    - "a tutorial on thompson sampling"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "a tutorial on thompson sampling"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "thompson sampling for the mnl bandit"
    - "simple bayesian algorithms for best arm identification"
    - "deep exploration via bootstrapped dqn"
    - "cascading bandits learning to rank in the cascade model"
    - "bayesian reinforcement learning a survey"
    - "learning to optimize via information directed sampling"

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)