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: "comparison of maximum likelihood and gan based training of real nvps"
  Abstract: We train a generator by maximum likelihood and we also train the same generator architecture by Wasserstein GAN. We then compare the generated samples, exact log-probability densities and approximate Wasserstein distances. We show that an independent critic trained to approximate Wasserstein distance between the validation set and the generator distribution helps detect overfitting. Finally, we use ideas from the one-shot learning literature to develop a novel fast learning critic.

Connected through hyperedge HE-1 (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"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"

Connected through hyperedge HE-2 (induced by source paper "alignflow cycle consistent learning from multiple domains via normalizing flows"):
  with members:
    - "domain adaptation with asymmetrically relaxed distribution alignment"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "video to video synthesis"
    - "neural autoregressive flows"
    - "universal neural machine translation for extremely low resource languages"
    - "multimodal unsupervised image to image translation"
    - "neural ordinary differential equations"
    - "generalization bounds for unsupervised cross domain mapping with wgans"

Connected through hyperedge HE-3 (induced by source paper "pointflow 3d point cloud generation with continuous normalizing flows"):
  with members:
    - "videoflow a flow based generative model for video"
    - "pointwise an unsupervised point wise feature learning network"
    - "waveglow a flow based generative network for speech synthesis"
    - "multiresolution tree networks for 3d point cloud processing"
    - "deep sets"
    - "adversarial autoencoders for compact representations of 3d point clouds"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "point cloud gan"

Connected through hyperedge HE-4 (induced by source paper "guided image generation with conditional invertible neural networks"):
  with members:
    - "videoflow a flow based generative model for video"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "training generative reversible networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-5 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "fair coresets and streaming algorithms for fair k means clustering"

Connected through hyperedge HE-6 (induced by source paper "justifying diagnosis decisions by deep neural networks"):
  with members:
    - "on the automatic generation of medical imaging reports"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "generating continuous representations of medical texts"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "a method to model conditional distributions with normalizing flows"):
  with members:
    - "generative latent flow a framework for non adversarial image generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "semantic image synthesis with spatially adaptive normalization"
    - "guided image generation with conditional invertible neural networks"
    - "analyzing inverse problems with invertible neural networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "density estimation using real nvp"

Connected through hyperedge HE-8 (induced by source paper "transferring gans generating images from limited data"):
  with members:
    - "cgans with projection discriminator"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "pros and cons of gan evaluation measures"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pose guided person image generation"
    - "progressive growing of gans for improved quality stability and variation"

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)