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: "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.

Connected through hyperedge HE-1 (induced by source paper "proximity forest an effective and scalable distance based classifier for time series"):
  with members:
    - "fast and accurate time series classification with weasel"
    - "a time series forest for classification and feature extraction"
    - "faster retrieval with a two pass dynamic time warping lower bound"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-2 (induced by source paper "on the separation of shape and temporal patterns in time series application to signature authentication"):
  with members:
    - "on recursive edit distance kernels with application to time series classification"
    - "a kernel for time series based on global alignments"

Connected through hyperedge HE-3 (induced by source paper "exact mean computation in dynamic time warping spaces"):
  with members:
    - "tight hardness results for consensus problems on circular strings and time series"
    - "nonsmooth analysis and subgradient methods for averaging in dynamic time warping spaces"
    - "optimal warping paths are unique for almost every pair of time series"
    - "fuzzy c shape a new algorithm for clustering finite time series waveforms"
    - "shapedtw shape dynamic time warping"
    - "on the existence of a sample mean in dynamic time warping spaces"
    - "dynamic time warping and geometric edit distance breaking the quadratic barrier"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"

Connected through hyperedge HE-4 (induced by source paper "sparsification of the alignment path search space in dynamic time warping"):
  with members:
    - "on recursive edit distance kernels with application to time series classification"
    - "an empirical evaluation of similarity measures for time series classification"
    - "sparsedtw a novel approach to speed up dynamic time warping"
    - "time warp edit distance with stiffness adjustment for time series matching"
    - "a kernel for time series based on global alignments"

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)