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: "stabilizing gan training with multiple random projections"
  Abstract: Training generative adversarial networks is unstable in high-dimensions as the true data distribution tends to be concentrated in a small fraction of the ambient space. The discriminator is then quickly able to classify nearly all generated samples as fake, leaving the generator without meaningful gradients and causing it to deteriorate after a point in training. In this work, we propose training a single generator simultaneously against an array of discriminators, each of which looks at a different random low-dimensional projection of the data. Individual discriminators, now provided with restricted views of the input, are unable to reject generated samples perfectly and continue to provide meaningful gradients to the generator throughout training. Meanwhile, the generator learns to produce samples consistent with the full data distribution to satisfy all discriminators simultaneously. We demonstrate the practical utility of this approach experimentally, and show that it is able to produce image samples with higher quality than traditional training with a single discriminator.

Connected through hyperedge HE-1 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "on convergence and stability of gans"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation through self supervision"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "domain generalization by solving jigsaw puzzles"
    - "boosting supervision with self supervision for few shot learning"
    - "deep visual domain adaptation a survey"
    - "unsupervised representation learning by predicting image rotations"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-3 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-4 (induced by source paper "synthesizing 3d shapes from silhouette image collections using multi projection generative adversarial networks"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "learning category specific mesh reconstruction from image collections"
    - "spectral normalization for generative adversarial networks"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-5 (induced by source paper "deconstructing generative adversarial networks"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "approximability of discriminators implies diversity in gans"
    - "a convex duality framework for gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the implicit assumptions of gans"
    - "solving approximate wasserstein gans to stationarity"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "multi adversarial variational autoencoder networks"):
  with members:
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "autoregressive quantile networks for generative modeling"
    - "learning implicit brain mri manifolds with deep learning"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "catgan coupled adversarial transfer for domain generation"

Connected through hyperedge HE-7 (induced by source paper "multi objective training of generative adversarial networks with multiple discriminators"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "on line adaptative curriculum learning for gans"
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"
    - "improved training of wasserstein gans"
    - "generative multi adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "depthwisegans fast training generative adversarial networks for realistic image synthesis"):
  with members:
    - "generative adversarial networks using adaptive convolution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mmgan manifold matching generative adversarial network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "the devil is in the decoder classification regression and gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

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)