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: "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
  Abstract: We consider a contextual version of multi-armed bandit problem with global knapsack constraints. In each round, the outcome of pulling an arm is a scalar reward and a resource consumption vector, both dependent on the context, and the global knapsack constraints require the total consumption for each resource to be below some pre-fixed budget. The learning agent competes with an arbitrary set of context-dependent policies. This problem was introduced by Badanidiyuru et al. (2014), who gave a computationally inefficient algorithm with near-optimal regret bounds for it. We give a computationally efficient algorithm for this problem with slightly better regret bounds, by generalizing the approach of Agarwal et al. (2014) for the non-constrained version of the problem. The computational time of our algorithm scales logarithmically in the size of the policy space. This answers the main open question of Badanidiyuru et al. (2014). We also extend our results to a variant where there are no knapsack constraints but the objective is an arbitrary Lipschitz concave function of the sum of outcome vectors.

Connected through hyperedge HE-1 (induced by source paper "delay adaptive learning in generalized linear contextual bandits"):
  with members:
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "thompson sampling for the mnl bandit"
    - "delay and cooperation in nonstochastic bandits"
    - "asymptotic convergence in online learning with unbounded delays"
    - "off policy evaluation for slate recommendation"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-2 (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"
    - "efficient learning in large scale combinatorial semi bandits"
    - "resourceful contextual bandits"
    - "bandits with knapsacks"

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"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"

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 "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-6 (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"

Connected through hyperedge HE-7 (induced by source paper "multi level feedback web links selection problem learning and optimization"):
  with members:
    - "collaborative filtering bandits"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-8 (induced by source paper "linear contextual bandits with knapsacks"):
  with members:
    - "near optimal online algorithms and fast approximation algorithms for resource allocation problems"
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "bandits with concave rewards and convex knapsacks"
    - "fast algorithms for online stochastic convex programming"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"
    - "online stochastic packing applied to display ad allocation"

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)