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 "is rotation forest the best classifier for problems with continuous features"):
  Abstract: Rotation forest is a tree based ensemble that performs transforms on subsets of attributes prior to constructing each tree. We present an empirical comparison of classifiers for problems with only real valued features. We evaluate classifiers from three families of algorithms: support vector machines; tree-based ensembles; and neural networks. We compare classifiers on unseen data based on the quality of the decision rule (using classification error) the ability to rank cases (area under the receiver operator curve) and the probability estimates (using negative log likelihood). We conclude that, in answer to the question posed in the title, yes, rotation forest, is significantly more accurate on average than competing techniques when compared on three distinct sets of datasets. The same pattern of results are observed when tuning classifiers on the train data using a grid search. We investigate why rotation forest does so well by testing whether the characteristics of the data can be used to differentiate classifier performance. We assess the impact of the design features of rotation forest through an ablative study that transforms random forest into rotation forest. We identify the major limitation of rotation forest as its scalability, particularly in number of attributes. To overcome this problem we develop a model to predict the train time of the algorithm and hence propose a contract version of rotation forest where a run time cap {\em a priori}. We demonstrate that on large problems rotation forest can be made an order of magnitude faster without significant loss of accuracy and that there is no real benefit (on average) from tuning the ensemble. We conclude that without any domain knowledge to indicate an algorithm preference, rotation forest should be the default algorithm of choice for problems with continuous attributes.

Includes members:
  Member "machine learning automation toolbox mlaut":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sktime a unified interface for machine learning with time series")
  Member "the uea multivariate time series classification archive 2018":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised scalable representation learning for multivariate time series")
  Member "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")
  Member "xgboost a scalable tree boosting system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach")
  Member "comparison of 14 different families of classification algorithms on 115 binary datasets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning automation toolbox mlaut")
  Member "should we really use post hoc tests based on mean ranks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking 1d cnn for time series classification a stronger baseline")
  Member "a time series forest for classification and feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for time series classification a review")

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)