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: "fast and accurate time series classification with weasel"
  Abstract: Time series (TS) occur in many scientific and commercial applications, ranging from earth surveillance to industry automation to the smart grids. An important type of TS analysis is classification, which can, for instance, improve energy load forecasting in smart grids by detecting the types of electronic devices based on their energy consumption profiles recorded by automatic sensors. Such sensor-driven applications are very often characterized by (a) very long TS and (b) very large TS datasets needing classification. However, current methods to time series classification (TSC) cannot cope with such data volumes at acceptable accuracy; they are either scalable but offer only inferior classification quality, or they achieve state-of-the-art classification quality but cannot scale to large data volumes. In this paper, we present WEASEL (Word ExtrAction for time SEries cLassification), a novel TSC method which is both fast and accurate. Like other state-of-the-art TSC methods, WEASEL transforms time series into feature vectors, using a sliding-window approach, which are then analyzed through a machine learning classifier. The novelty of WEASEL lies in its specific method for deriving features, resulting in a much smaller yet much more discriminative feature set. On the popular UCR benchmark of 85 TS datasets, WEASEL is more accurate than the best current non-ensemble algorithms at orders-of-magnitude lower classification and training times, and it is almost as accurate as ensemble classifiers, whose computational complexity makes them inapplicable even for mid-size datasets. The outstanding robustness of WEASEL is also confirmed by experiments on two real smart grid datasets, where it out-of-the-box achieves almost the same accuracy as highly tuned, domain-specific methods.

Connected through hyperedge HE-1 (induced by source paper "inceptiontime finding alexnet for time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "automatic alignment of surgical videos using kinematic data"
    - "deep neural network ensembles for time series classification"
    - "adversarial attacks on deep neural networks for time series classification"
    - "a cnn adapted to time series for the classification of supernovae"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "transfer learning for time series classification"

Connected through hyperedge HE-2 (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"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "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-3 (induced by source paper "rocket exceptionally fast and accurate time series classification using random convolutional kernels"):
  with members:
    - "unsupervised scalable representation learning for multivariate time series"
    - "inceptiontime finding alexnet for time series classification"
    - "deep neural network ensembles for time series classification"
    - "time series classification for varying length series"
    - "ts chief a scalable and accurate forest algorithm for time series classification"
    - "the ucr time series archive"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-4 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification"):
  with members:
    - "scalable dictionary classifiers for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "from bop to boss and beyond time series classification with dictionary based classifiers"
    - "scalable and accurate deep learning for electronic health records"
    - "the ucr time series archive"

Connected through hyperedge HE-5 (induced by source paper "waveletfcnn a deep time series classification model for wind turbine blade icing detection"):
  with members:
    - "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"
    - "are generative deep models for novelty detection truly better"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "wavelet convolutional neural networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks on time series"):
  with members:
    - "multivariate lstm fcns for time series classification"
    - "deep learning for time series classification a review"
    - "lstm fully convolutional networks for time series classification"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "audio adversarial examples targeted attacks on speech to text"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-7 (induced by source paper "timage a robust time series classification pipeline"):
  with members:
    - "insights into lstm fully convolutional networks for time series classification"
    - "deep learning for time series classification a review"
    - "transfer learning for time series classification"
    - "lstm fully convolutional networks for time series classification"
    - "classification of time series images using deep convolutional neural networks"
    - "imaging time series to improve classification and imputation"
    - "deep residual learning for image recognition"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"

Connected through hyperedge HE-8 (induced by source paper "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency"):
  with members:
    - "scalable dictionary classifiers for time series classification"
    - "sktime a unified interface for machine learning with time series"
    - "ts chief a scalable and accurate forest algorithm for time series classification"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "the ucr time series archive"
    - "is rotation forest the best classifier for problems with continuous features"
    - "a time series forest for classification and feature extraction"
    - "time warp edit distance with stiffness adjustment for time series matching"

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.DS(Data Structures and Algorithms)