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: "introduction to multi armed bandits"
  Abstract: Multi-armed bandits a simple but very powerful framework for algorithms that make decisions over time under uncertainty. An enormous body of work has accumulated over the years, covered in several books and surveys. This book provides a more introductory, textbook-like treatment of the subject. Each chapter tackles a particular line of work, providing a self-contained, teachable technical introduction and a brief review of the further developments. The chapters are as follows: stochastic bandits, lower bounds; Bayesian bandits and Thompson Sampling; Lipschitz Bandits; full Feedback and adversarial costs; adversarial bandits; linear costs and semi-bandits; contextual Bandits; bandits and games; bandits with knapsacks; bandits and incentives.

Connected through hyperedge HE-1 (induced by source paper "effective diversity in population based reinforcement learning"):
  with members:
    - "novelty search for deep reinforcement learning policy network weights by action sequence edit metric distance"
    - "collaborative evolutionary reinforcement learning"
    - "provably robust blackbox optimization for reinforcement learning"
    - "behavior guided reinforcement learning"
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "adapting behaviour for learning progress"
    - "attraction repulsion actor critic for continuous control reinforcement learning"
    - "a generalized framework for population based training"

Connected through hyperedge HE-2 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"
    - "bounded regret for finite armed structured bandits"

Connected through hyperedge HE-3 (induced by source paper "selfish robustness and equilibria in multi player bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "competing bandits in matching markets"
    - "multiplayer bandits without observing collision information"
    - "dynamic pricing with finitely many unknown valuations"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-4 (induced by source paper "incentivizing exploration with selective data disclosure"):
  with members:
    - "sample complexity of incentivized exploration"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "descending price optimally coordinates search"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"

Connected through hyperedge HE-5 (induced by source paper "advances in bandits with knapsacks"):
  with members:
    - "unifying the stochastic and the adversarial bandits with knapsack"
    - "adversarial bandits with knapsacks"
    - "combinatorial semi bandits with knapsacks"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "efficient learning in large scale combinatorial semi bandits"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"

Connected through hyperedge HE-6 (induced by source paper "sample complexity of incentivized exploration"):
  with members:
    - "overabundant information and learning traps"
    - "optimal and myopic information acquisition"
    - "descending price optimally coordinates search"
    - "bayesian incentive compatible bandit exploration"
    - "economic recommendation systems"
    - "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"
    - "bandits with knapsacks"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-7 (induced by source paper "regret bounds for batched bandits"):
  with members:
    - "batch policy learning under constraints"
    - "adaptivity in adaptive submodularity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "stochastic submodular cover with limited adaptivity"
    - "delay and cooperation in nonstochastic bandits"
    - "batched gaussian process bandit optimization via determinantal point processes"
    - "online learning with switching costs and other adaptive adversaries"
    - "parallel gaussian process optimization with upper confidence bound and pure exploration"

Connected through hyperedge HE-8 (induced by source paper "on thompson sampling for smoother than lipschitz bandits"):
  with members:
    - "thompson sampling and approximate inference"
    - "thompson sampling for combinatorial semi bandits"
    - "a tutorial on thompson sampling"
    - "learning to optimize via posterior sampling"
    - "bandits and experts in metric spaces"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "x armed bandits"

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)