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: "improving mmd gan training with repulsive loss function"
  Abstract: Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data. To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD. Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function. The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets. Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions. The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.

Connected through hyperedge HE-1 (induced by source paper "autogan neural architecture search for generative adversarial networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "controllable artistic text style transfer via shape matching gan"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "enlightengan deep light enhancement without paired supervision"
    - "the gan landscape losses architectures regularization and normalization"
    - "the relativistic discriminator a key element missing from standard gan"
    - "amc automl for model compression and acceleration on mobile devices"
    - "dada deep adversarial data augmentation for extremely low data regime classification"

Connected through hyperedge HE-2 (induced by source paper "adversarialnas adversarial neural architecture search for gans"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "agan towards automated design of generative adversarial networks"
    - "efficient neural architecture transformation searchin channel level for object detection"
    - "autogan neural architecture search for generative adversarial networks"
    - "one shot neural architecture search via self evaluated template network"
    - "customizable architecture search for semantic segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"

Connected through hyperedge HE-3 (induced by source paper "kernel mean matching for content addressability of gans"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "which training methods for gans do actually converge"
    - "progressive growing of gans for improved quality stability and variation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a mean field theory for kernel alignment with random features in generative adversarial networks"):
  with members:
    - "a solvable high dimensional model of gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
    - "mode regularized generative adversarial networks"
    - "generative moment matching networks"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-5 (induced by source paper "attending category disentangled global context for image classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "exploring disentangled feature representation beyond face identification"
    - "learn to pay attention"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "residual attention network for image classification"
    - "fader networks manipulating images by sliding attributes"

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)