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: "deep neural network ensembles for time series classification"
  Abstract: Deep neural networks have revolutionized many fields such as computer vision and natural language processing. Inspired by this recent success, deep learning started to show promising results for Time Series Classification (TSC). However, neural networks are still behind the state-of-the-art TSC algorithms, that are currently composed of ensembles of 37 non deep learning based classifiers. We attribute this gap in performance due to the lack of neural network ensembles for TSC. Therefore in this paper, we show how an ensemble of 60 deep learning models can significantly improve upon the current state-of-the-art performance of neural networks for TSC, when evaluated over the UCR/UEA archive: the largest publicly available benchmark for time series analysis. Finally, we show how our proposed Neural Network Ensemble (NNE) is the first time series classifier to outperform COTE while reaching similar performance to the current state-of-the-art ensemble HIVE-COTE.

Connected through hyperedge HE-1 (induced by source paper "residual attention net for superior cross domain time sequence modeling"):
  with members:
    - "insights into lstm fully convolutional networks for time series classification"
    - "multivariate lstm fcns for time series classification"
    - "ts chief a scalable and accurate forest algorithm for time series classification"
    - "star transformer"
    - "universal transformers"
    - "linked dynamic graph cnn learning on point cloud via linking hierarchical features"
    - "financial series prediction using attention lstm"
    - "weighted transformer network for machine translation"

Connected through hyperedge HE-2 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks"):
  with members:
    - "multivariate lstm fcns for time series classification"
    - "tsviz demystification of deep learning models for time series analysis"
    - "ecg heartbeat classification a deep transferable representation"
    - "lstm fully convolutional networks for time series classification"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "axiomatic attribution for deep networks"
    - "towards better understanding of gradient based attribution methods for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning for cancer detection of lymph node metastases"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"
    - "make nearly every neural network better generating neural network ensembles by weight parameter resampling"
    - "rotation equivariant cnns for digital pathology"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (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"
    - "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"
    - "the ucr time series archive"

Connected through hyperedge HE-5 (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"
    - "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"
    - "fast and accurate time series classification with weasel"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks on deep neural networks for time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "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"
    - "adversarial examples for evaluating reading comprehension systems"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "adversarial examples in the physical world"

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)