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 "short isometric shapelet transform for binary time series classification"):
  Abstract: In the research area of time series classification (TSC), ensemble shapelet transform (ST) algorithm is one of state-of-the-art algorithms for classification. However, the time complexity of it is often higher than other algorithms. Hence, two strategies of reducing the high time complexity are proposed in this paper. The first one is to only exploit shapelet candidates whose length is a given small value, whereas the ensemble ST uses shapelet candidates of all the feasible lengths. The second one is to train a single linear classifier in the feature space, whereas the ensemble ST requires an ensemble classifier trained in the feature space. This paper focuses on the theoretical evidences and the empirical implementation of these two strategies. The theoretical part guarantees a near-lossless accuracy under some preconditions while reducing the time complexity. In the empirical part, an algorithm is proposed as a model implementation of these two strategies. The superior performance of the proposed algorithm on some experiments shows the effectiveness of these two strategies.

Includes members:
  Member "fast and accurate time series classification with weasel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inceptiontime finding alexnet for time series classification")
  Member "a time series forest for classification and feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for time series classification a review")
  Member "time warp edit distance with stiffness adjustment for time series matching":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an overview of distance and similarity functions for structured data")

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)