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: "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.

Connected through hyperedge HE-1 (induced by source paper "online pricing with offline data phase transition and inverse square law"):
  with members:
    - "empirical bayes regret minimization"
    - "optimal exploitation of clustering and history information in multi armed bandit"
    - "adaptive sequential experiments with unknown information flows"
    - "bandits with knapsacks"

Connected through hyperedge HE-2 (induced by source paper "hedging the drift learning to optimize under non stationarity"):
  with members:
    - "a new algorithm for non stationary contextual bandits efficient optimal and parameter free"
    - "reinforcement learning under drift"
    - "model selection for contextual bandits"
    - "learning to optimize under non stationarity"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "tracking the best expert in non stationary stochastic environments"
    - "corralling a band of bandit algorithms"
    - "online optimization competing with dynamic comparators"

Connected through hyperedge HE-3 (induced by source paper "adaptive sequential experiments with unknown information flows"):
  with members:
    - "bandit learning with positive externalities"
    - "estimating the causal impact of recommendation systems from observational data"
    - "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "real time bidding benchmarking with ipinyou dataset"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "real time bid optimization with smooth budget delivery in online advertising"
    - "further optimal regret bounds for thompson sampling"

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)