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 "adversarial attacks on linear contextual bandits"):
  Abstract: Contextual bandit algorithms are applied in a wide range of domains, from advertising to recommender systems, from clinical trials to education. In many of these domains, malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior. For instance, an unscrupulous ad publisher may try to increase their own revenue at the expense of the advertisers; a seller may want to increase the exposure of their products, or thwart a competitor's advertising campaign. In this paper, we study several attack scenarios and show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm $T - o(T)$ times over a horizon of $T$ steps, while applying adversarial modifications to either rewards or contexts that only grow logarithmically as $O(\log T)$. We also investigate the case when a malicious agent is interested in affecting the behavior of the bandit algorithm in a single context (e.g., a specific user). We first provide sufficient conditions for the feasibility of the attack and we then propose an efficient algorithm to perform the attack. We validate our theoretical results on experiments performed on both synthetic and real-world datasets.

Includes members:
  Member "stochastic linear optimization with adversarial corruption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "data poisoning attacks on stochastic bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "corruption robust exploration in episodic reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable self play algorithms for competitive reinforcement learning")
  Member "better algorithms for stochastic bandits with adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents")
  Member "targeted attacks on deep reinforcement learning agents through adversarial observations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "manipulating machine learning poisoning attacks and countermeasures for regression learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "data poisoning attacks in contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "adversarial bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advances in bandits with knapsacks")

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)