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: "contextual bandits with global constraints and objective"
  Abstract: We consider the contextual version of a multi-armed bandit problem with global convex constraints and concave objective function. In each round, the outcome of pulling an arm is a context-dependent vector, and the global constraints require the average of these vectors to lie in a certain convex set. The objective is a concave function of this average vector. The learning agent competes with an arbitrary set of context-dependent policies. This problem is a common generalization of problems considered by Badanidiyuru et al. (2014) and Agrawal and Devanur (2014), with important applications. We give computationally efficient algorithms with near-optimal regret, generalizing the approach of Agarwal et al. (2014) for the non-constrained version of the problem. For the special case of budget constraints our regret bounds match those of Badanidiyuru et al. (2014), answering their main open question of obtaining a computationally efficient algorithm.

Connected through hyperedge HE-1 (induced by source paper "matching while learning"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "linear contextual bandits with global constraints and objective"
    - "bandits with concave rewards and convex knapsacks"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "resourceful contextual bandits"
    - "an information theoretic analysis of thompson sampling"
    - "dynamic matching market design"
    - "bandits with knapsacks"

Connected through hyperedge HE-2 (induced by source paper "risk aware algorithms for adversarial contextual bandits"):
  with members:
    - "improved regret bounds for oracle based adversarial contextual bandits"
    - "linear contextual bandits with global constraints and objective"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "contextual bandit algorithms with supervised learning guarantees"

Connected through hyperedge HE-3 (induced by source paper "algorithms with logarithmic or sublinear regret for constrained contextual bandits"):
  with members:
    - "thompson sampling for budgeted multi armed bandits"
    - "a survey on contextual multi armed bandits"
    - "linear contextual bandits with global constraints and objective"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "bandits with knapsacks"
    - "optimal rate sampling in 802 11 systems"

Connected through hyperedge HE-4 (induced by source paper "adaptive contract design for crowdsourcing markets bandit algorithms for repeated principal agent problems"):
  with members:
    - "incentivizing high quality crowdwork"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"
    - "bandit algorithms for tree search"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"
    - "toward a classification of finite partial monitoring games"

Connected through hyperedge HE-5 (induced by source paper "resourceful contextual bandits"):
  with members:
    - "linear contextual bandits with global constraints and objective"
    - "bandits with concave rewards and convex knapsacks"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "knapsack based optimal policies for budget limited multi armed bandits"
    - "approximation algorithms for correlated knapsacks and non martingale bandits"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "contextual bandits with similarity information"
    - "multi armed bandits in metric spaces"

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)