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: "diversity sensitive conditional generative adversarial networks"
  Abstract: We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an input is always mapped to a single output regardless of variations in latent code. To address such issue, we propose to explicitly regularize the generator to produce diverse outputs depending on latent codes. The proposed regularization is simple, general, and can be easily integrated into most conditional GAN objectives. Additionally, explicit regularization on generator allows our method to control a balance between visual quality and diversity. We demonstrate the effectiveness of our method on three conditional generation tasks: image-to-image translation, image inpainting, and future video prediction. We show that simple addition of our regularization to existing models leads to surprisingly diverse generations, substantially outperforming the previous approaches for multi-modal conditional generation specifically designed in each individual task.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "unsupervised domain specific deblurring via disentangled representations"
    - "noise2self blind denoising by self supervision"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-2 (induced by source paper "noise robust generative adversarial networks"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "noise2self blind denoising by self supervision"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "self supervised gans via auxiliary rotation loss"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-3 (induced by source paper "on the evaluation of conditional gans"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "classification accuracy score for conditional generative models"
    - "from variational to deterministic autoencoders"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"

Connected through hyperedge HE-4 (induced by source paper "point to point video generation"):
  with members:
    - "phasenet for video frame interpolation"
    - "conditional video generation using action appearance captions"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "self supervised learning of a facial attribute embedding from video"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-5 (induced by source paper "updates leak data set inference and reconstruction attacks in online learning"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "under the hood of membership inference attacks on aggregate location time series"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "exploiting unintended feature leakage in collaborative learning"
    - "knockoff nets stealing functionality of black box models"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "cost sensitive robustness against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior"):
  with members:
    - "a surprisingly effective fix for deep latent variable modeling of text"
    - "implicit deep latent variable models for text generation"
    - "learning variations in human motion via mix and match perturbation"
    - "mixture content selection for diverse sequence generation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "mixture models for diverse machine translation tricks of the trade"
    - "videoflow a flow based generative model for video"

Connected through hyperedge HE-7 (induced by source paper "stochastic conditional generative networks with basis decomposition"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "tafe net task aware feature embeddings for low shot learning"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "texturegan controlling deep image synthesis with texture patches"
    - "mathematics of deep learning"
    - "few shot image recognition by predicting parameters from activations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "improving sample diversity of a pre trained class conditional gan by changing its class embeddings"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "on the steerability of generative adversarial networks"
    - "understanding neural networks via feature visualization a survey"
    - "pros and cons of gan evaluation measures"
    - "transferring gans generating images from limited data"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "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)