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 "times series averaging and denoising from a probabilistic perspective on time elastic kernels"):
  Abstract: In the light of regularized dynamic time warping kernels, this paper re-considers the concept of time elastic centroid for a set#R##N#of time series. We derive a new algorithm based on a probabilistic interpretation of kernel alignment matrices. This algorithm expresses#R##N#the averaging process in terms of a stochastic alignment automata. It uses an iterative agglomerative heuristic method for averaging#R##N#the aligned samples, while also averaging the times of occurrence of the aligned samples. By comparing classification accuracies for#R##N#45 heterogeneous time series datasets obtained by first nearest centroid/medoid classifiers we show that: i) centroid-based#R##N#approaches significantly outperform medoid-based approaches, ii) for the considered datasets, our algorithm that combines averaging#R##N#in the sample space and along the time axes, emerges as the most significantly robust model for time-elastic averaging with a#R##N#promising noise reduction capability. We also demonstrate its benefit in an isolated gesture recognition experiment and its ability to#R##N#significantly reduce the size of training instance sets. Finally we highlight its denoising capability using demonstrative synthetic data:#R##N#we show that it is possible to retrieve, from few noisy instances, a signal whose components are scattered in a wide spectral band.

Includes members:
  Member "on recursive edit distance kernels with application to time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi view time series classification via global local correlative channel aware fusion mechanism")
  Member "time warp edit distance with stiffness adjustment for time series matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an overview of distance and similarity functions for structured data")
  Member "a kernel for time series based on global alignments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition")

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)