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 "know your customer multi armed bandits with capacity constraints"):
  Abstract: A wide range of resource allocation and platform operation settings exhibit the following two simultaneous challenges: (1) service resources are capacity constrained; and (2) clients' preferences are not perfectly known. To study this pair of challenges, we consider a service system with heterogeneous servers and clients. Server types are known and there is fixed capacity of servers of each type. Clients arrive over time, with types initially unknown and drawn from some distribution. Each client sequentially brings $N$ jobs before leaving. The system operator assigns each job to some server type, resulting in a payoff whose distribution depends on the client and server types. #R##N#Our main contribution is a complete characterization of the structure of the optimal policy for maximization of the rate of payoff accumulation. Such a policy must balance three goals: (i) earning immediate payoffs; (ii) learning client types to increase future payoffs; and (iii) satisfying the capacity constraints. We construct a policy that has provably optimal regret (to leading order as $N$ grows large). Our policy has an appealingly simple three-phase structure: a short type-"guessing" phase, a type-"confirmation" phase that balances payoffs with learning, and finally an "exploitation" phase that focuses on payoffs. Crucially, our approach employs the shadow prices of the capacity constraints in the assignment problem with known types as "externality prices" on the servers' capacity.

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 "resourceful contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "advances in bandits with knapsacks")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploration exploitation in constrained mdps")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "dynamic pricing with limited supply":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in bandits with knapsacks")
  Member "settling the polynomial learnability of mixtures of gaussians":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when can wasserstein gans minimize wasserstein distance")

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)