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: "proximity forest an effective and scalable distance based classifier for time series"
  Abstract: Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10,000 time series only; which may explain why the primary research focus has been on creating algorithms that have high accuracy on relatively small datasets. This paper introduces Proximity Forest, an algorithm that learns accurate models from datasets with millions of time series, and classifies a time series in milliseconds. The models are ensembles of highly randomized Proximity Trees. Whereas conventional decision trees branch on attribute values (and usually perform poorly on time series), Proximity Trees branch on the proximity of time series to one exemplar time series or another; allowing us to leverage the decades of work into developing relevant measures for time series. Proximity Forest gains both efficiency and accuracy by stochastic selection of both exemplars and similarity measures. Our work is motivated by recent time series applications that provide orders of magnitude more time series than the UCR benchmarks. Our experiments demonstrate that Proximity Forest is highly competitive on the UCR archive: it ranks among the most accurate classifiers while being significantly faster. We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state-of-the-art models Elastic Ensemble and COTE.

Connected through hyperedge HE-1 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"

Connected through hyperedge HE-2 (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"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "transfer learning for time series classification"
    - "the ucr time series archive"

Connected through hyperedge HE-3 (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"
    - "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"
    - "fast and accurate time series classification with weasel"

Connected through hyperedge HE-4 (induced by source paper "deep neural network ensembles for time series classification"):
  with members:
    - "adversarial attacks on deep neural networks for time series classification"
    - "deep learning for time series classification a review"
    - "deep neural network ensemble by data augmentation and bagging for skin lesion classification"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "towards a universal neural network encoder for time series"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-5 (induced by source paper "sktime a unified interface for machine learning with time series"):
  with members:
    - "machine learning automation toolbox mlaut"
    - "a tale of two toolkits report the first benchmarking time series classification algorithms for correctness and efficiency"
    - "deep learning for time series classification a review"
    - "gluonts probabilistic time series models in python"
    - "hctsa a computational framework for automated time series phenotyping using massive feature extraction"
    - "learning reductions that really work"
    - "a time series forest for classification and feature extraction"
    - "api design for machine learning software experiences from the scikit learn project"

Connected through hyperedge HE-6 (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"
    - "the ucr time series archive"
    - "is rotation forest the best classifier for problems with continuous features"
    - "fast and accurate time series classification with weasel"
    - "a time series forest for classification and feature extraction"
    - "time warp edit distance with stiffness adjustment for time series matching"

Connected through hyperedge HE-7 (induced by source paper "scalable dictionary classifiers for time series classification"):
  with members:
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"
    - "the heterogeneous ensembles of standard classification algorithms hesca the whole is greater than the sum of its parts"
    - "should we really use post hoc tests based on mean ranks"
    - "a time series forest for classification and feature extraction"

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)