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 "meta dynamic pricing learning across experiments"):
  Abstract: We study the problem of learning shared structure across a sequence of dynamic pricing experiments for related products. We consider a practical formulation where the unknown demand parameters for each product come from an unknown distribution (prior) that is shared across products. We then propose a meta dynamic pricing algorithm that learns this prior online while solving a sequence of Thompson sampling pricing experiments (each with horizon T) for N different products. Our algorithm addresses two challenges: (i) balancing the need to learn the prior (meta-exploration) with the need to leverage the estimated prior to achieve good performance (meta-exploitation), and (ii) accounting for uncertainty in the estimated prior by appropriately ``widening" the prior as a function of its estimation error, thereby ensuring convergence of each price experiment. Unlike prior-independent approaches, our algorithm's meta regret grows sublinearly in N; an immediate consequence of our analysis is that the price of an unknown prior in Thompson sampling is negligible in experiment-rich environments with shared structure (large N). Numerical experiments on synthetic and real auto loan data demonstrate that our algorithm significantly speeds up learning compared to prior-independent algorithms or a naive approach of greedily using the updated prior across products.

Includes members:
  Member "regret bounds for meta bayesian optimization with an unknown gaussian process prior":
    also connected through other hyperedges:
      (none)
  Member "learning to route efficiently with end to end feedback the value of networked structure":
    also connected through other hyperedges:
      (none)
  Member "meta learning for contextual bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning efficient and effective exploration policies with counterfactual meta policy")
  Member "probabilistic model agnostic meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a network pruning network approach to deep model compression")
  Member "a tutorial on thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "model agnostic meta learning for fast adaptation of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "on the prior sensitivity of thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bandit learning through biased maximum likelihood estimation")
  Member "bandits with concave rewards and convex knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploration exploitation in constrained mdps")

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)