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: "classification of time series images using deep convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

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"
    - "imaging time series to improve classification and imputation"
    - "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 "self attention for raw optical satellite time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "massively parallel hyperparameter tuning"
    - "the uea multivariate time series classification archive 2018"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tune a research platform for distributed model selection and training"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "the ucr time series archive"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "bag of recurrence patterns representation for time series classification"):
  with members:
    - "dense bag of temporal sift words for time series classification"
    - "scalable classification of repetitive time series through frequencies of local polynomials"
    - "classifiers with a reject option for early time series classification"
    - "bag of words representation for biomedical time series classification"
    - "ecoc based training of neural networks for face recognition"

Connected through hyperedge HE-6 (induced by source paper "human action recognition using deep multilevel multimodal m2 fusion of depth and inertial sensors"):
  with members:
    - "lstm a search space odyssey"
    - "a tutorial on canonical correlation methods"
    - "real time human motion capture with multiple depth cameras"

Connected through hyperedge HE-7 (induced by source paper "magnetic resonance spectroscopy quantification using deep learning"):
  with members:
    - "quantification of metabolites in magnetic resonance spectroscopic imaging using machine learning"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "caffe convolutional architecture for fast feature embedding"

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.CV(Computer Vision and Pattern Recognition)