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 "the intrinsic robustness of stochastic bandits to strategic manipulation"):
  Abstract: We study the behavior of stochastic bandits algorithms under \emph{strategic behavior} conducted by rational actors, i.e., the arms. Each arm is a strategic player who can modify its own reward whenever pulled, subject to a cross-period budget constraint. Each arm is \emph{self-interested} and seeks to maximize its own expected number of times of being pulled over a decision horizon. Strategic manipulations naturally arise in various economic applications, e.g., recommendation systems such as Yelp and Amazon. We analyze the robustness of three popular bandit algorithms: UCB, $\varepsilon$-Greedy, and Thompson Sampling. We prove that all three algorithms achieve a regret upper bound $\mathcal{O}(\max \{ B, \ln T\})$ under \emph{any} (possibly adaptive) strategy of the strategic arms, where $B$ is the total budget across arms. Moreover, we prove that our regret upper bound is \emph{tight}. Our results illustrate the intrinsic robustness of bandits algorithms against strategic manipulation so long as $B=o(T)$. This is in sharp contrast to the more pessimistic model of adversarial attacks where an attack budget of $\mathcal{O}(\ln T) $ can trick UCB and $\varepsilon$-Greedy to pull the optimal arm only $o(T)$ number of times. Our results hold for both bounded and unbounded rewards.

Includes members:
  Member "online learning for measuring incentive compatibility in ad auctions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "envy regret and social welfare loss")
  Member "bayesian exploration with heterogeneous agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "incentivising exploration and recommendations for contextual bandits with payments")
  Member "stochastic bandits robust to adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "learning to bid without knowing your value":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks")
  Member "multi armed bandit problems with strategic arms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "bayesian incentive compatible bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effective diversity in population based reinforcement learning")

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)