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: "linear contextual bandits with knapsacks"
  Abstract: We consider the linear contextual bandit problem with resource consumption, in addition to reward generation. In each round, the outcome of pulling an arm is a reward as well as a vector of resource consumptions. The expected values of these outcomes depend linearly on the context of that arm. The budget/capacity constraints require that the total consumption doesn't exceed the budget for each resource. The objective is once again to maximize the total reward. This problem turns out to be a common generalization of classic linear contextual bandits (linContextual), bandits with knapsacks (BwK), and the online stochastic packing problem (OSPP). We present algorithms with near-optimal regret bounds for this problem. Our bounds compare favorably to results on the unstructured version of the problem where the relation between the contexts and the outcomes could be arbitrary, but the algorithm only competes against a fixed set of policies accessible through an optimization oracle. We combine techniques from the work on linContextual, BwK, and OSPP in a nontrivial manner while also tackling new difficulties that are not present in any of these special cases.

Connected through hyperedge HE-1 (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"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "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-2 (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"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"

Connected through hyperedge HE-3 (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"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-4 (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-5 (induced by source paper "incorporating behavioral constraints in online ai systems"):
  with members:
    - "ai safety gridworlds"
    - "context attentive bandits contextual bandit with restricted context"
    - "the social dilemma of autonomous vehicles"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "playing atari with deep reinforcement learning"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "combinatorial semi bandits with knapsacks"):
  with members:
    - "dcm bandits learning to rank with multiple clicks"
    - "cascading bandits for large scale recommendation problems"
    - "efficient algorithms for adversarial contextual learning"
    - "contextual semibandits via supervised learning oracles"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"
    - "bandits with concave rewards and convex knapsacks"
    - "matroid bandits fast combinatorial optimization with learning"

Connected through hyperedge HE-7 (induced by source paper "assortment optimization under unknown multinomial logit choice models"):
  with members:
    - "knapsack based optimal policies for budget limited multi armed bandits"

Connected through hyperedge HE-8 (induced by source paper "bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "adaptive crowdsourcing algorithms for the bandit survey problem"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "budget optimization for sponsored search censored learning in mdps"

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)