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 "strategic classification from revealed preferences"):
  Abstract: We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome. In rounds, the learner deploys a classifier, and an adversarially chosen agent arrives, possibly manipulating her features to optimally respond to the learner. The learner has no knowledge of the agents' utility functions or "real" features, which may vary widely across agents. Instead, the learner is only able to observe their "revealed preferences" --- i.e. the actual manipulated feature vectors they provide. For a broad family of agent cost functions, we give a computationally efficient learning algorithm that is able to obtain diminishing "Stackelberg regret" --- a form of policy regret that guarantees that the learner is obtaining loss nearly as small as that of the best classifier in hindsight, even allowing for the fact that agents will best-respond differently to the optimal classifier.

Includes members:
  Member "kernel based methods for bandit convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "multidimensional dynamic pricing for welfare maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "watch and learn optimizing from revealed preferences feedback":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "learning from rational behavior predicting solutions to unknown linear programs":
    also connected through other hyperedges:
      (none)
  Member "learning economic parameters from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "online learning and profit maximization from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "efficiently learning from revealed preference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "online convex optimization in the bandit setting gradient descent without a gradient":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accelerating reinforcement learning with a directional gaussian smoothing evolution strategy")

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)