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 "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"):
  Abstract: The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses asymmetric weighted averaging as update rule. Empirical results exhibited superior performance of asymmetric generalized LVQ (GLVQ) over other state-of-the-art prototype generation methods for nearest neighbor classification.

Includes members:
  Member "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "optimal warping paths are unique for almost every pair of time series":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trainable time warping aligning time series in the continuous time domain")
  Member "on the existence of a sample mean in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "a reduction theorem for the sample mean in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces")
  Member "a review of learning vector quantization classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient computation of counterfactual explanations of lvq models")
  Member "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "short range audio channels security survey of mechanisms applications and research challenges")

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)