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: "mmd gan towards deeper understanding of moment matching network"
  Abstract: Generative moment matching network (GMMN) is a deep generative model that differs from Generative Adversarial Network (GAN) by replacing the discriminator in GAN with a two-sample test based on kernel maximum mean discrepancy (MMD). Although some theoretical guarantees of MMD have been studied, the empirical performance of GMMN is still not as competitive as that of GAN on challenging and large benchmark datasets. The computational efficiency of GMMN is also less desirable in comparison with GAN, partially due to its requirement for a rather large batch size during the training. In this paper, we propose to improve both the model expressiveness of GMMN and its computational efficiency by introducing adversarial kernel learning techniques, as the replacement of a fixed Gaussian kernel in the original GMMN. The new approach combines the key ideas in both GMMN and GAN, hence we name it MMD GAN. The new distance measure in MMD GAN is a meaningful loss that enjoys the advantage of weak topology and can be optimized via gradient descent with relatively small batch sizes. In our evaluation on multiple benchmark datasets, including MNIST, CIFAR- 10, CelebA and LSUN, the performance of MMD-GAN significantly outperforms GMMN, and is competitive with other representative GAN works.

Connected through hyperedge HE-1 (induced by source paper "medgan medical image translation using gans"):
  with members:
    - "generative adversarial network in medical imaging a review"
    - "towards a deeper understanding of adversarial losses"
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "adversarial sparse view cbct artifact reduction"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "cross modality synthesis from ct to pet using fcn and gan networks for improved automated lesion detection"
    - "pros and cons of gan evaluation measures"

Connected through hyperedge HE-2 (induced by source paper "opengan open set generative adversarial networks"):
  with members:
    - "few shot adversarial learning of realistic neural talking head models"
    - "the importance of metric learning for robotic vision open set recognition and active learning"
    - "cgans with projection discriminator"
    - "mc gan multi conditional generative adversarial network for image synthesis"
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "spectral normalization for generative adversarial networks"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-3 (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-4 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-5 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "unsupervised program synthesis for images using tree structured lstm"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "learning to infer and execute 3d shape programs"
    - "write execute assess program synthesis with a repl"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "3d aware scene manipulation via inverse graphics"
    - "a tree based decoder for neural machine translation"
    - "automatic program synthesis of long programs with a learned garbage collector"

Connected through hyperedge HE-7 (induced by source paper "learning implicit generative models with theoretical guarantees"):
  with members:
    - "deep generative learning via variational gradient flow"
    - "sinkhorn autoencoders"
    - "wasserstein wasserstein auto encoders"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "generative modeling using the sliced wasserstein distance"

Connected through hyperedge HE-8 (induced by source paper "improve sgd training via aligning min batches"):
  with members:
    - "convolutional networks with dense connectivity"
    - "cold case the lost mnist digits"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "deep learning for classical japanese literature"
    - "escaping saddles with stochastic gradients"
    - "exploring generalization in deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)