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 global constraints and objective"
  Abstract: We consider the linear contextual bandit problem with global convex constraints and a concave objective function. In each round, the outcome of pulling an arm is a vector, that depends linearly on the context of that arm. 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. This problem turns out to be a common generalization of classic linear contextual bandits (linContextual) [Auer 2003], bandits with concave rewards and convex knapsacks (BwCR) [Agrawal, Devanur 2014], and the online stochastic convex programming (OSCP) problem [Agrawal, Devanur 2015]. We present algorithms with near-optimal regret bounds for this problem. Our bounds compare favorably to results on the unstructured version of the problem [Agrawal et al. 2015, Badanidiyuru et al. 2014] where the relation between the contexts and the outcomes could be arbitrary, but the algorithm only competes against a fixed set of policies.

Connected through hyperedge HE-1 (induced by source paper "matching while learning"):
  with members:
    - "algorithms with logarithmic or sublinear regret for constrained contextual bandits"
    - "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"
    - "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"
    - "contextual bandits with global constraints and objective"
    - "a survey on contextual multi armed bandits"
    - "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 "resourceful contextual bandits"):
  with members:
    - "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)