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: "imaging time series to improve classification and imputation"
  Abstract: Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summation/Difference Fields (GASF/GADF) and Markov Transition Fields (MTF). This enables the use of techniques from computer vision for time series classification and imputation. We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images. Our approaches achieve highly competitive results when compared to nine of the current best time series classification approaches. Inspired by the bijection property of GASF on 0/1 rescaled data, we train Denoised Auto-encoders (DA) on the GASF images of four standard and one synthesized compound dataset. The imputation MSE on test data is reduced by 12.18%-48.02% when compared to using the raw data. An analysis of the features and weights learned via tiled CNNs and DAs explains why the approaches work.

Connected through hyperedge HE-1 (induced by source paper "implementation of deep neural networks to classify eeg signals using gramian angular summation field for epilepsy diagnosis"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "classification of time series images using deep convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "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"

Connected through hyperedge HE-3 (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"
    - "fast and accurate time series classification with weasel"
    - "classification of time series images using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "musical instrument sound classification with deep convolutional neural network using feature fusion approach"

Connected through hyperedge HE-4 (induced by source paper "non technical loss detection with statistical profile images based on semi supervised learning"):
  with members:
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "temporal ensembling for semi supervised learning"
    - "large scale detection of non technical losses in imbalanced data sets"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "shortening time required for adaptive structural learning method of deep belief network with multi modal data arrangement"):
  with members:
    - "an adaptive learning method of restricted boltzmann machine by neuron generation and annihilation algorithm"
    - "adaptive learning method of recurrent temporal deep belief network to analyze time series data"
    - "an adaptive learning method of deep belief network by layer generation algorithm"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "time series classification from scratch with deep neural networks a strong baseline"):
  with members:
    - "wide deep learning for recommender systems"
    - "multi scale convolutional neural networks for time series classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning deep features for discriminative localization"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "representation learning with deconvolution for multivariate time series classification and visualization"):
  with members:
    - "deep generative stochastic networks trainable by backprop"

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)