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 "a tale of two toolkits report the second bake off redux chapter 1 dictionary based classifiers"):
  Abstract: Time series classification (TSC) is the problem of learning labels from time dependent data. One class of algorithms is derived from a bag of words approach. A window is run along a series, the subseries is shortened and discretised to form a word, then features are formed from the histogram of frequency of occurrence of words. We call this type of approach to TSC dictionary based classification. We compare four dictionary based algorithms in the context of a wider project to update the great time series classification bakeoff, a comparative study published in 2017. We experimentally characterise the algorithms in terms of predictive performance, time complexity and space complexity. We find that we can improve on the previous best in terms of accuracy, but this comes at the cost of time and space. Alternatively, the same performance can be achieved with far less cost. We review the relative merits of the four algorithms before suggesting a path to possible improvement.

Includes members:
  Member "scalable dictionary classifiers for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification")
  Member "sktime a unified interface for machine learning with time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency")
  Member "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sktime a unified interface for machine learning with time series")
  Member "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")
  Member "fast and accurate time series classification with weasel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "inceptiontime finding alexnet for time series classification")
  Member "simulated data experiments for time series classification part 1 accuracy comparison with default settings":
    also connected through other hyperedges:
      (none)
  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")

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)