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: "bandits with concave rewards and convex knapsacks"
  Abstract: In this paper, we consider a very general model for exploration-exploitation tradeoff which allows arbitrary concave rewards and convex constraints on the decisions across time, in addition to the customary limitation on the time horizon. This model subsumes the classic multi-armed bandit (MAB) model, and the Bandits with Knapsacks (BwK) model of Badanidiyuru et al.[2013]. We also consider an extension of this model to allow linear contexts, similar to the linear contextual extension of the MAB model. We demonstrate that a natural and simple extension of the UCB family of algorithms for MAB provides a polynomial time algorithm that has near-optimal regret guarantees for this substantially more general model, and matches the bounds provided by Badanidiyuru et al.[2013] for the special case of BwK, which is quite surprising. We also provide computationally more efficient algorithms by establishing interesting connections between this problem and other well studied problems/algorithms such as the Blackwell approachability problem, online convex optimization, and the Frank-Wolfe technique for convex optimization. We give examples of several concrete applications, where this more general model of bandits allows for richer and/or more efficient formulations of the problem.

Connected through hyperedge HE-1 (induced by source paper "exploration exploitation in constrained mdps"):
  with members:
    - "conservative exploration in reinforcement learning"
    - "improved algorithms for conservative exploration in bandits"
    - "optimistic policy optimization with bandit feedback"
    - "constrained upper confidence reinforcement learning"
    - "a modern introduction to online learning"
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "online convex optimization in adversarial markov decision processes"
    - "provably efficient exploration in policy optimization"

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"
    - "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-3 (induced by source paper "meta dynamic pricing learning across experiments"):
  with members:
    - "regret bounds for meta bayesian optimization with an unknown gaussian process prior"
    - "learning to route efficiently with end to end feedback the value of networked structure"
    - "meta learning for contextual bandit exploration"
    - "probabilistic model agnostic meta learning"
    - "a tutorial on thompson sampling"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "on the prior sensitivity of thompson sampling"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-4 (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"
    - "resourceful contextual bandits"

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"
    - "resourceful contextual bandits"
    - "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 "dynamic pricing with finitely many unknown valuations"):
  with members:
    - "fast rates for bandit optimization with upper confidence frank wolfe"
    - "online learning in repeated auctions"
    - "perfect bayesian equilibria in repeated sales"
    - "bandits with knapsacks"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

Connected through hyperedge HE-8 (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 knapsacks"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "the best of both worlds stochastic and adversarial 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)