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 "tops ensemble learning with trees of predictors"):
  Abstract: We present a new approach to ensemble learning. Our approach differs from previous approaches in that it constructs and applies different predictive models to different subsets of the feature space. It does this by constructing a tree of subsets of the feature space and associating a predictor (predictive model) to each node of the tree; we call the resulting object a  tree of predictors . The (locally) optimal tree of predictors is derived recursively; each step involves  jointly optimizing  the split of the terminal nodes of the previous tree and the choice of learner (from among a given set of base learners) and training set—hence predictor—for each set in the split. The features of a new instance determine a unique path through the optimal tree of predictors; the final prediction aggregates the predictions of the predictors along this path. Thus, our approach uses base learners to create complex learners that are matched to the characteristics of the data set while avoiding overfitting. We establish loss bounds for the final predictor in terms of the Rademacher complexity of the base learners. We report the results of a number of experiments on a variety of datasets, showing that our approach provides statistically significant improvements over a wide variety of state-of-the-art machine learning algorithms, including various ensemble learning methods.

Includes members:
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "adaptive ensemble learning with confidence bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploiting relevance for online decision making in high dimensions")
  Member "ensemble of distributed learners for online classification of dynamic data streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptive ensemble learning with confidence bounds")

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.CV(Computer Vision and Pattern Recognition)