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: "multivariate lstm fcns for time series classification"
  Abstract: Abstract   Over the past decade, multivariate time series classification has received great attention. We propose transforming the existing univariate time series classification models, the Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series classification model by augmenting the fully convolutional block with a squeeze-and-excitation block to further improve accuracy. Our proposed models outperform most state-of-the-art models while requiring minimum preprocessing. The proposed models work efficiently on various complex multivariate time series classification tasks such as activity recognition or action recognition. Furthermore, the proposed models are highly efficient at test time and small enough to deploy on memory constrained systems.

Connected through hyperedge HE-1 (induced by source paper "residual attention net for superior cross domain time sequence modeling"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "insights into lstm fully convolutional networks 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 "fully convolutional networks for structural health monitoring through multivariate time series classification"):
  with members:
    - "insights into lstm fully convolutional networks for time series classification"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "summertime variable length time seriessummarization with applications to physicalactivity analysis"):
  with members:
    - "toeplitz inverse covariance based clustering of multivariate time series data"

Connected through hyperedge HE-4 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks"):
  with members:
    - "deep neural network ensembles 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-5 (induced by source paper "improving robustness of time series classifier with neural ode guided gradient based data augmentation"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "adversarial attacks on deep neural networks for time series classification"
    - "insights into lstm fully convolutional networks for time series classification"
    - "deep learning for time series classification a review"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "object detection with deep learning a review"

Connected through hyperedge HE-6 (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"
    - "lstm fully convolutional networks for time series classification"
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"
    - "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-7 (induced by source paper "multi view time series classification via global local correlative channel aware fusion mechanism"):
  with members:
    - "ev action electromyography vision multi modal action dataset"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "memory fusion network for multi view sequential learning"
    - "multi scale convolutional neural networks for time series classification"
    - "temporal segment networks towards good practices for deep action recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on recursive edit distance kernels with application to time series classification"

Connected through hyperedge HE-8 (induced by source paper "adversarial attacks on time series"):
  with members:
    - "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"
    - "fast and accurate time series classification with weasel"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "time series classification from scratch with deep neural networks a strong baseline"

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)