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 "adaptive ensemble learning with confidence bounds"):
  Abstract: Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, we show that our proposed method outperforms all existing ensemble learning techniques, even in the presence of concept drift. We illustrate the performance of Hedged Bandits in the context of medical informatics. However, the proposed methods have numerous other applications, including network monitoring and security, online recommendation systems, social networks, smart cities, etc.

Includes members:
  Member "distributed constrained optimization by consensus based primal dual perturbation method":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grand challenges of resilience autonomous system resilience through design and runtime measures")
  Member "follow the leader if you can hedge if you must":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast rates for online prediction with abstention")
  Member "learning to optimize via posterior sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "distributed online learning via cooperative contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "ensemble of distributed learners for online classification of dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "tops ensemble learning with trees of predictors")
  Member "learning under concept drift an overview":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "models for predicting community specific interest in news articles")
  Member "a parameter free hedging algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a modern introduction to online 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)