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 "corrupted multidimensional binary search learning in the presence of irrational agents"):
  Abstract: Standard game-theoretic formulations for settings like contextual pricing and security games assume that agents act in accordance with a specific behavioral model. In practice however, some agents may not prescribe to the dominant behavioral model or may act in ways that are arbitrarily inconsistent. Existing algorithms heavily depend on the model being (approximately) accurate for all agents and have poor performance in the presence of even a few such arbitrarily irrational agents. \emph{How do we design learning algorithms that are robust to the presence of arbitrarily irrational agents?} We address this question for a number of canonical game-theoretic applications by designing a robust algorithm for the fundamental problem of multidimensional binary search. The performance of our algorithm degrades gracefully with the number of corrupted rounds, which correspond to irrational agents and need not be known in advance. As binary search is the key primitive in algorithms for contextual pricing, Stackelberg Security Games, and other game-theoretic applications, we immediately obtain robust algorithms for these settings. Our techniques draw inspiration from learning theory, game theory, high-dimensional geometry, and convex analysis, and may be of independent algorithmic interest.

Includes members:
  Member "stochastic linear optimization with adversarial corruption":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial attacks on linear contextual bandits")
  Member "corruption robust exploration in episodic reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "adversarial attacks on linear contextual bandits")
  Member "the intrinsic robustness of stochastic bandits to strategic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "semi parametric dynamic contextual pricing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "protecting consumers against personalized pricing a stopping time approach")
  Member "manipulating a learning defender and ways to counteract":
    also connected through other hyperedges:
      (none)
  Member "contextual search via intrinsic volumes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions")
  Member "stochastic bandits robust to adversarial corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophet inequalities with linear correlations and augmentations")

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)