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 "shapedtw shape dynamic time warping"):
  Abstract: Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does not necessarily achieve locally sensible matchings. Concretely, two temporal points with entirely dissimilar local structures may be matched by DTW. To address this problem, we propose an improved alignment algorithm, named shape Dynamic Time Warping (shapeDTW), which enhances DTW by taking point-wise local structural information into consideration. shapeDTW is inherently a DTW algorithm, but additionally attempts to pair locally similar structures and to avoid matching points with distinct neighborhood structures. We apply shapeDTW to align audio signal pairs having ground-truth alignments, as well as artificially simulated pairs of aligned sequences, and obtain quantitatively much lower alignment errors than DTW and its two variants. When shapeDTW is used as a distance measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series, it beats DTW on 64 out of 84 UCR time series datasets, with significantly improved classification accuracies. By using a properly designed local structure descriptor, shapeDTW improves accuracies by more than 10% on 18 datasets. To the best of our knowledge, shapeDTW is the first distance measure under the nearest neighbor classifier scheme to significantly outperform DTW, which had been widely recognized as the best distance measure to date. Our code is publicly accessible at: this https URL

Includes members:
  Member "the great time series classification bake off an experimental evaluation of recently proposed algorithms extended version":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extracting vehicle sensor signals from can logs for driver re identification")
  Member "continuous action recognition based on sequence alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online real time multiple spatiotemporal action localisation and prediction")
  Member "an experimental evaluation of nearest neighbour time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "application of machine learning to accidents detection at directional drilling")
  Member "invariant time series factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "from bop to boss and beyond time series classification with dictionary based classifiers")
  Member "metric learning for temporal sequence alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neuralwarp time series similarity with warping networks")
  Member "a time series forest for classification and feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for time series classification a review")
  Member "sdtw computing dtw distances using locally relevant constraints based on salient feature alignments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dense bag of temporal sift words for time series classification")
  Member "faster retrieval with a two pass dynamic time warping lower bound":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate nearest neighbor for curves simple efficient and deterministic")

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.CV(Computer Vision and Pattern Recognition)