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: "improved training with curriculum gans"
  Abstract: In this paper we introduce Curriculum GANs, a curriculum learning strategy for training Generative Adversarial Networks that increases the strength of the discriminator over the course of training, thereby making the learning task progressively more difficult for the generator. We demonstrate that this strategy is key to obtaining state-of-the-art results in image generation. We also show evidence that this strategy may be broadly applicable to improving GAN training in other data modalities.

Connected through hyperedge HE-1 (induced by source paper "a u net based discriminator for generative adversarial networks"):
  with members:
    - "consistency regularization for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "coco gan generation by parts via conditional coordinating"
    - "mixmatch a holistic approach to semi supervised learning"
    - "cgans with projection discriminator"
    - "the gan landscape losses architectures regularization and normalization"
    - "self supervised gans via auxiliary rotation loss"
    - "pa gan improving gan training by progressive augmentation"

Connected through hyperedge HE-2 (induced by source paper "curriculum deepsdf"):
  with members:
    - "implicit geometric regularization for learning shapes"
    - "learning shape templates with structured implicit functions"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "on the power of curriculum learning in training deep networks"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning to infer implicit surfaces without 3d supervision"
    - "deep structured implicit functions"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"

Connected through hyperedge HE-3 (induced by source paper "learning to inpaint by progressively growing the mask regions"):
  with members:
    - "foreground aware image inpainting"
    - "competence based curriculum learning for neural machine translation"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "free form image inpainting with gated convolution"
    - "a style based generator architecture for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "curriculum learning and minibatch bucketing in neural machine translation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "curriculum based dropout discriminator for domain adaptation"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "attending to discriminative certainty for domain adaptation"
    - "looking back at labels a class based domain adaptation technique"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "differential attention for visual question answering"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "multi adversarial domain adaptation"
    - "dual discriminator generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "a style based generator architecture for generative adversarial networks"):
  with members:
    - "cgans with projection discriminator"
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "generative adversarial interpolative autoencoding adversarial training on latent space interpolations encourages convex latent distributions"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "progressive recurrent learning for visual recognition"):
  with members:
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "low shot learning from imaginary data"
    - "sampleahead online classifier sampler communication for learning from synthesized data"
    - "dynamic few shot visual learning without forgetting"
    - "progressive neural architecture search"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "progressive neural 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)