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 "making the dynamic time warping distance warping invariant"):
  Abstract: The literature postulates that the dynamic time warping (dtw) distance can cope with temporal variations but stores and processes time series in a form as if the dtw-distance cannot cope with such variations. To address this inconsistency, we first show that the dtw-distance is not warping-invariant. The lack of warping-invariance contributes to the inconsistency mentioned above and to a strange behavior. To eliminate these peculiarities, we convert the dtw-distance to a warping-invariant semi-metric, called time-warp-invariant (twi) distance. Empirical results suggest that the error rates of the twi and dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense. However, the twi-distance requires less storage and computation time than the dtw-distance for a broad range of problems. These results challenge the current practice of applying the dtw-distance in nearest-neighbor classification and suggest the proposed twi-distance as a more efficient and consistent option.

Includes members:
  Member "hardness of consensus problems for circular strings and time series averaging":
    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 "the ucr time series archive":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable hybrid hmm with gaussian process emission for sequential time series data clustering")
  Member "nonsmooth analysis and subgradient methods for averaging 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 "exact mean computation in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "insights into lstm fully convolutional networks for time series classification")
  Member "on the existence of a sample mean in dynamic time warping spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an average compress algorithm for the sample mean problem under dynamic time warping")
  Member "faster retrieval with a two pass dynamic time warping lower bound":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.LG(Machine Learning)