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: "memorization precedes generation learning unsupervised gans with memory networks"
  Abstract: We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space. Second, discriminators of GANs easily forget about past generated samples by generators, incurring instability during adversarial training. We argue that these two infamous problems of unsupervised GAN training can be largely alleviated by a learnable memory network to which both generators and discriminators can access. Generators can effectively learn representation of training samples to understand underlying cluster distributions of data, which ease the structure discontinuity problem. At the same time, discriminators can better memorize clusters of previously generated samples, which mitigate the forgetting problem. We propose a novel end-to-end GAN model named memoryGAN, which involves a memory network that is unsupervisedly trainable and integrable to many existing GAN models. With evaluations on multiple datasets such as Fashion-MNIST, CelebA, CIFAR10, and Chairs, we show that our model is probabilistically interpretable, and generates realistic image samples of high visual fidelity. The memoryGAN also achieves the state-of-the-art inception scores over unsupervised GAN models on the CIFAR10 dataset, without any optimization tricks and weaker divergences.

Connected through hyperedge HE-1 (induced by source paper "memory augmented generative adversarial networks for anomaly detection"):
  with members:
    - "regularized cycle consistent generative adversarial network for anomaly detection"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "anomaly detection with multiple hypotheses predictions"
    - "adversarially learned anomaly detection"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast and provable admm for learning with generative priors"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "algorithmic aspects of inverse problems using generative models"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "phase retrieval under a generative prior"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "adversarial audio synthesis"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"

Connected through hyperedge HE-4 (induced by source paper "coloring with limited data few shot colorization via memory augmented networks"):
  with members:
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "coloring with words guiding image colorization through text based palette generation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-5 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial networks gans what it can generate and what it cannot"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "evolutionary generative adversarial networks"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "comparative study on generative adversarial networks"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "the cramer distance as a solution to biased wasserstein gradients"

Connected through hyperedge HE-7 (induced by source paper "self supervised gans via auxiliary rotation loss"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "cgans with projection discriminator"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the gan landscape losses architectures regularization and normalization"
    - "on catastrophic forgetting and mode collapse in generative adversarial networks"
    - "deep clustering for unsupervised learning of visual features"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "self supervised generative adversarial networks"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "the gan landscape losses architectures regularization and normalization"
    - "on catastrophic forgetting and mode collapse in generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised representation learning by predicting image rotations"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"

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)