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 "data poisoning attacks on stochastic bandits"):
  Abstract: Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many others. Even though potential attacks against these learning algorithms may hijack their behavior, causing catastrophic loss in real-world applications, little is known about adversarial attacks on bandit algorithms. In this paper, we propose a framework of offline attacks on bandit algorithms and study convex optimization based attacks on several popular bandit algorithms. We show that the attacker can force the bandit algorithm to pull a target arm with high probability by a slight manipulation of the rewards in the data. Then we study a form of online attacks on bandit algorithms and propose an adaptive attack strategy against any bandit algorithm without the knowledge of the bandit algorithm. Our adaptive attack strategy can hijack the behavior of the bandit algorithm to suffer a linear regret with only a logarithmic cost to the attacker. Our results demonstrate a significant security threat to stochastic bandits.

Includes members:
  Member "better algorithms for stochastic bandits with adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial attacks on linear contextual bandits")
  Member "adversarial attacks on stochastic bandits":
    also connected through other hyperedges:
      (none)
  Member "stochastic bandits robust to adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "prophet inequalities with linear correlations and augmentations")
  Member "ucboost a boosting approach to tame complexity and optimality for stochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerated learning from recommender systems using multi armed bandit")
  Member "data poisoning attacks in contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "is feature selection secure against training data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "data poisoning attacks against online learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on defending against label flipping attacks on malware detection systems")
  Member "adversarial attacks on neural network policies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep 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)