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: "quick and easy time series generation with established image based gans"
  Abstract: In the recent years Generative Adversarial Networks (GANs) have demonstrated significant progress in generating authentic looking data. In this work we introduce our simple method to exploit the advancements in well established image-based GANs to synthesise single channel time series data. We implement Wasserstein GANs (WGANs) with gradient penalty due to their stability in training to synthesise three different types of data; sinusoidal data, photoplethysmograph (PPG) data and electrocardiograph (ECG) data. The length of the returned time series data is limited only by the image resolution, we use an image size of 64x64 pixels which yields 4096 data points. We present both visual and quantitative evidence that our novel method can successfully generate time series data using image-based GANs.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"
    - "the relativistic discriminator a key element missing from standard gan"

Connected through hyperedge HE-2 (induced by source paper "a neuro ai interface for evaluating generative adversarial networks"):
  with members:
    - "generative adversarial networks in computer vision a survey and taxonomy"
    - "generative adversarial networks a survey and taxonomy"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "openloris object a dataset and benchmark towards lifelong object recognition"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pros and cons of gan evaluation measures"
    - "an empirical study on evaluation metrics of generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "controlled time series generation for automotive software in the loop testing using gans"):
  with members:
    - "sensegen a deep learning architecture for synthetic sensor data generation"
    - "improved techniques for training gans"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "an overview of gradient descent optimization algorithms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-4 (induced by source paper "synthetic neuroscore using a neuro ai interface for evaluating generative adversarial networks"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pros and cons of gan evaluation measures"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "use of neural signals to evaluate the quality of generative adversarial network performance in facial image generation"
    - "generative image inpainting with contextual attention"
    - "began boundary equilibrium generative adversarial networks"

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)