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: "figr few shot image generation with reptile"
  Abstract: Generative Adversarial Networks (GAN) boast impressive capacity to generate realistic images. However, like much of the field of deep learning, they require an inordinate amount of data to produce results, thereby limiting their usefulness in generating novelty. In the same vein, recent advances in meta-learning have opened the door to many few-shot learning applications. In the present work, we propose Few-shot Image Generation using Reptile (FIGR), a GAN meta-trained with Reptile. Our model successfully generates novel images on both MNIST and Omniglot with as little as 4 images from an unseen class. We further contribute FIGR-8, a new dataset for few-shot image generation, which contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8, initial results show that our model can generalize to more advanced concepts (such as "bird" and "knife") from as few as 8 samples from a previously unseen class of images and as little as 10 training steps through those 8 images. This work demonstrates the potential of training a GAN for few-shot image generation and aims to set a new benchmark for future work in the domain.

Connected through hyperedge HE-1 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stabilizing gans with octave convolutions"
    - "semi few shot attribute translation"
    - "band limited training and inference for convolutional neural networks"
    - "unmasking deepfakes with simple features"
    - "a fourier perspective on model robustness in computer vision"
    - "attributing fake images to gans learning and analyzing gan fingerprints"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"

Connected through hyperedge HE-2 (induced by source paper "matchinggan matching based few shot image generation"):
  with members:
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few shot unsupervised image to image translation"
    - "cgans with projection discriminator"
    - "on first order meta learning algorithms"
    - "spectral normalization for generative adversarial networks"
    - "meta transfer learning for few shot learning"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"
    - "online learning with gated linear networks"

Connected through hyperedge HE-4 (induced by source paper "semi few shot attribute translation"):
  with members:
    - "video to video synthesis"
    - "on first order meta learning algorithms"
    - "recycle gan unsupervised video retargeting"
    - "multimodal unsupervised image to image translation"
    - "how to train your maml"
    - "adversarial information factorization"
    - "probabilistic model agnostic meta learning"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-5 (induced by source paper "a learned representation for scalable vector graphics"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "attention is all you need"
    - "film visual reasoning with a general conditioning layer"
    - "learning to infer graphics programs from hand drawn images"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-6 (induced by source paper "artistic glyph image synthesis via one stage few shot learning"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "few shot unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "tet gan text effects transfer via stylization and destylization"
    - "the contextual loss for image transformation with non aligned data"
    - "image to image translation for cross domain disentanglement"
    - "style and content disentanglement in generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-7 (induced by source paper "metalgan multi domain label less image synthesis using cgans and meta learning"):
  with members:
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "metalgan a cluster based adaptive training for few shot adversarial colorization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to optimize neural nets"
    - "dna gan learning disentangled representations from multi attribute images"
    - "attgan facial attribute editing by only changing what you want"
    - "learning to discover cross domain relations with 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)