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: "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.

Connected through hyperedge HE-1 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense"):
  with members:
    - "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "stochastic bandits robust to adversarial corruptions"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "data poisoning attacks on factorization based collaborative filtering"

Connected through hyperedge HE-2 (induced by source paper "adversarial attacks on linear contextual bandits"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "targeted attacks on deep reinforcement learning agents through adversarial observations"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "data poisoning attacks in contextual bandits"
    - "adversarial bandits with knapsacks"
    - "a comparative study of matrix factorization and random walk with restart in recommender systems"

Connected through hyperedge HE-3 (induced by source paper "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"):
  with members:
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "stochastic bandits robust to adversarial corruptions"
    - "an improved parametrization and analysis of the exp3 algorithm for stochastic and adversarial bandits"
    - "an algorithm with nearly optimal pseudo regret for both stochastic and adversarial bandits"
    - "algorithms for multi armed bandit problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "decentralized online learning algorithms for opportunistic spectrum access"

Connected through hyperedge HE-4 (induced by source paper "prediction with corrupted expert advice"):
  with members:
    - "introduction to online convex optimization"

Connected through hyperedge HE-5 (induced by source paper "policy poisoning in batch reinforcement learning and control"):
  with members:
    - "interactive teaching algorithms for inverse reinforcement learning"
    - "online data poisoning attack"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "data poisoning attacks against online learning"
    - "machine teaching for inverse reinforcement learning algorithms and applications"
    - "adversarial attacks on neural network policies"

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)