human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "insights into lstm fully convolutional networks for time series classification"):
  with members:
    - "deep gated recurrent and convolutional network hybrid model for univariate time series classification"
    - "multivariate lstm fcns for time series classification"
    - "lstm fully convolutional networks for time series classification"
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"
    - "squeeze and excitation networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "multi scale convolutional neural networks for time series classification"

Connected through hyperedge HE-2 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "adam a method for stochastic optimization"
    - "on the relation between the common labelling and the median graph"

Connected through hyperedge HE-3 (induced by source paper "making the dynamic time warping distance warping invariant"):
  with members:
    - "hardness of consensus problems for circular strings and time series averaging"
    - "the ucr time series archive"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "faster retrieval with a two pass dynamic time warping lower bound"

Connected through hyperedge HE-4 (induced by source paper "trainable time warping aligning time series in the continuous time domain"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "optimal warping paths are unique for almost every pair of time series"
    - "adam a method for stochastic optimization"
    - "voice recognition algorithms using mel frequency cepstral coefficient mfcc and dynamic time warping dtw techniques"

Connected through hyperedge HE-5 (induced by source paper "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"):
  with members:
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "a reduction theorem for the sample mean in dynamic time warping spaces"
    - "practical recommendations for gradient based training of deep architectures"

Connected through hyperedge HE-6 (induced by source paper "optimal warping paths are unique for almost every pair of time series"):
  with members:
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "exact mean computation in dynamic time warping spaces"
    - "warped linear models for time series classification"

Question: Please tell me which class the center node 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)