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 "ensemble of distributed learners for online classification of dynamic data streams"):
  Abstract: We present an efficient distributed online learning scheme to classify data captured from distributed, heterogeneous, and dynamic data sources. Our scheme consists of multiple distributed local learners, that analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner and combined using a weighted majority rule to output the final prediction. We propose a novel online ensemble learning algorithm to update the aggregation rule in order to adapt to the underlying data dynamics. We rigorously determine a bound for the worst case misclassification probability of our algorithm which depends on the misclassification probabilities of the best static aggregation rule, and of the best local classifier. Importantly, the worst case misclassification probability of our algorithm tends asymptotically to 0 if the misclassification probability of the best static aggregation rule or the misclassification probability of the best local classifier tend to 0. Then we extend our algorithm to address challenges specific to the distributed implementation and we prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets. When applied to data sets widely used by the literature dealing with dynamic data streams and concept drift, our scheme exhibits performance gains ranging from 34% to 71% with respect to state of the art solutions.

Includes members:
  Member "cross layer design of distributed sensing estimation with quality feedback part i optimal schemes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale spectrum sensing in dense multi cell cognitive networks")
  Member "popular ensemble methods an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batchensemble an alternative approach to efficient ensemble and lifelong 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)