human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "algorithms with logarithmic or sublinear regret for constrained contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem")
  Member "bandits with concave rewards and convex knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploration exploitation in constrained mdps")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "resourceful contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advances in bandits with knapsacks")
  Member "how the experts algorithm can help solve lps online":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online assortment with limited inventories and set dependent revenues")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "online decision making in crowdsourcing markets theoretical challenges position paper":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives")
  Member "a near optimal dynamic learning algorithm for online matching problems with concave returns":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bernoulli factories and black box reductions in mechanism design")

Question: Please tell me which class the source paper 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)