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: "knapsack based optimal policies for budget limited multi armed bandits"
  Abstract: In budget-limited multi-armed bandit (MAB) problems, the learner's actions are costly and constrained by a fixed budget. Consequently, an optimal exploitation policy may not be to pull the optimal arm repeatedly, as is the case in other variants of MAB, but rather to pull the sequence of different arms that maximises the agent's total reward within the budget. This difference from existing MABs means that new approaches to maximising the total reward are required. Given this, we develop two pulling policies, namely: (i) KUBE; and (ii) fractional KUBE. Whereas the former provides better performance up to 40% in our experimental settings, the latter is computationally less expensive. We also prove logarithmic upper bounds for the regret of both policies, and show that these bounds are asymptotically optimal (i.e. they only differ from the best possible regret by a constant factor).

Connected through hyperedge HE-1 (induced by source paper "advances in bandits with knapsacks"):
  with members:
    - "introduction to multi armed bandits"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "budget constrained bandits over general cost and reward distributions"):
  with members:
    - "combinatorial semi bandits with knapsacks"
    - "thompson sampling for budgeted multi armed bandits"
    - "linear contextual bandits with knapsacks"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "a farewell to arms sequential reward maximization on a budget with a giving up option"):
  with members:
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "logarithmic regret bounds for bandits with knapsacks"
    - "online learning in repeated auctions"
    - "bandits with knapsacks"
    - "online learning under delayed feedback"

Connected through hyperedge HE-4 (induced by source paper "procrastinating with confidence near optimal anytime adaptive algorithm configuration"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "bandits with knapsacks"

Connected through hyperedge HE-5 (induced by source paper "multi armed bandit for energy efficient and delay sensitive edge computing in dynamic networks with uncertainty"):
  with members:
    - "computation offloading and activation of mobile edge computing servers a minority game"
    - "multi armed bandits with application to 5g small cells"
    - "thompson sampling for budgeted multi armed bandits"
    - "optimization of radio and computational resources for energy efficiency in latency constrained application offloading"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-6 (induced by source paper "adversarial bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "introduction to online convex optimization"
    - "introduction to multi armed bandits"
    - "bandit convex optimization for scalable and dynamic iot management"
    - "acceleration through optimistic no regret dynamics"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "unifying the stochastic and the adversarial bandits with knapsack"

Connected through hyperedge HE-7 (induced by source paper "unifying the stochastic and the adversarial bandits with knapsack"):
  with members:
    - "stochastic bandits robust to adversarial corruptions"
    - "budget constrained multi armed bandits with multiple plays"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "combinatorial semi bandits with knapsacks"
    - "bandits with concave rewards and convex knapsacks"
    - "bandits with knapsacks"
    - "the best of both worlds stochastic and adversarial bandits"

Connected through hyperedge HE-8 (induced by source paper "multi armed bandits with compensation"):
  with members:
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "thompson sampling for budgeted multi armed bandits"
    - "further optimal regret bounds for thompson sampling"
    - "contextual bandit algorithms with supervised learning guarantees"

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.AI(Artificial Intelligence)