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 "adaptive sequential experiments with unknown information flows"):
  Abstract: When information is limited, online recommendation services aim to strike a balance between maximizing immediate payoffs based on available information, and acquiring new information that is essential for maximizing future payoffs. This trade-off is captured by the multi-armed bandit (MAB) framework that has been studied and applied for designing sequential experiments when at each time epoch a single observation is collected on the action that was selected at that epoch. However, in many cases, additional information may become available between decision epochs. We introduce a generalized MAB formulation in which auxiliary information may appear arbitrarily over time. By obtaining matching lower and upper bounds, we characterize the minimax complexity of this family of problems as a function of the information arrival process, and study how salient characteristics of this process impact policy design and achievable performance.#R##N#We establish that while Thompson sampling and UCB policies leverage additional information naturally, other policies, such as epsilon-greedy, do not exhibit such robustness. We introduce a virtual time indexes method for dynamically controlling the exploration rate of policies, and apply it for designing epsilon-greedy-type policies that, without any prior knowledge on the information arrival process, attain the best performance (in terms of regret rate) that is achievable when the information arrival process is a priori known. We use data from a large media site to empirically analyze the value that may be captured in practice by leveraging auxiliary information flows for content recommendations. We further apply our method to a contextual bandit framework that allows for personalized recommendations using idiosyncratic consumer characteristics.

Includes members:
  Member "meta dynamic pricing learning across experiments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online pricing with offline data phase transition and inverse square law")
  Member "bandit learning with positive externalities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal dynamic information provision in traffic routing")
  Member "estimating the causal impact of recommendation systems from observational data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficiently learning and sampling interventional distributions from observations")
  Member "optimal exploration exploitation in a multi armed bandit problem with non stationary rewards":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regime switching bandits")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "real time bidding benchmarking with ipinyou dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable bid landscape forecasting in real time bidding")
  Member "empirical analysis of predictive algorithms for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "real time bid optimization with smooth budget delivery in online advertising":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")

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)