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: "resourceful contextual bandits"
  Abstract: We study contextual bandits with ancillary constraints on resources, which are common in real-world applications such as choosing ads or dynamic pricing of items. We design the first algorithm for solving these problems that handles constrained resources other than time, and improves over a trivial reduction to the non-contextual case. We consider very general settings for both contextual bandits (arbitrary policy sets, e.g. Dudik et al. (UAI'11)) and bandits with resource constraints (bandits with knapsacks, Badanidiyuru et al. (FOCS'13)), and prove a regret guarantee with near-optimal statistical properties.

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"
    - "bandits with knapsacks"

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"
    - "bandits with knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "exploration exploitation trade off in reinforcement learning on online markov decision processes with global concave rewards"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "regret bounds for reinforcement learning via markov chain concentration"
    - "adversarial bandits with knapsacks"
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "multi objective bandits optimizing the generalized gini index"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"

Connected through hyperedge HE-4 (induced by source paper "linear stochastic bandits under safety constraints"):
  with members:
    - "stagewise safe bayesian optimization with gaussian processes"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "constrained policy optimization"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "linear contextual bandits with knapsacks"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with knapsacks"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-5 (induced by source paper "thompson sampling for contextual bandit problems with auxiliary safety constraints"):
  with members:
    - "linear stochastic bandits under safety constraints"
    - "thompson sampling for the mnl bandit"
    - "deep kernel learning"
    - "bandits with concave rewards and convex knapsacks"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "a contextual bandit approach to personalized news article recommendation"

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 "learning to advertise with adaptive exposure via constrained two level reinforcement learning"):
  with members:
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"

Connected through hyperedge HE-8 (induced by source paper "learning adaptive display exposure for real time advertising"):
  with members:
    - "deep reinforcement learning for sponsored search real time bidding"
    - "reward constrained policy optimization"
    - "reinforcement learning to rank in e commerce search engine formalization analysis and application"
    - "budget constrained bidding by model free reinforcement learning in display advertising"
    - "hindsight experience replay"
    - "reinforcement mechanism design for e commerce"
    - "real time bidding by reinforcement learning in display advertising"
    - "constrained policy optimization"

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)