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: "generative modeling by estimating gradients of the data distribution"
  Abstract: We introduce a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. Because gradients can be ill-defined and hard to estimate when the data resides on low-dimensional manifolds, we perturb the data with different levels of Gaussian noise, and jointly estimate the corresponding scores, i.e., the vector fields of gradients of the perturbed data distribution for all noise levels. For sampling, we propose an annealed Langevin dynamics where we use gradients corresponding to gradually decreasing noise levels as the sampling process gets closer to the data manifold. Our framework allows flexible model architectures, requires no sampling during training or the use of adversarial methods, and provides a learning objective that can be used for principled model comparisons. Our models produce samples comparable to GANs on MNIST, CelebA and CIFAR-10 datasets, achieving a new state-of-the-art inception score of 8.87 on CIFAR-10. Additionally, we demonstrate that our models learn effective representations via image inpainting experiments.

Connected through hyperedge HE-1 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling"):
  with members:
    - "logan latent optimisation for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "maximum entropy generators for energy based models"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "autoregressive quantile networks for generative modeling"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-2 (induced by source paper "source separation with deep generative priors"):
  with members:
    - "music source separation in the waveform domain"
    - "end to end music source separation is it possible in the waveform domain"
    - "single image reflection separation with perceptual losses"
    - "neural separation of observed and unobserved distributions"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "dynamic routing between capsules"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-3 (induced by source paper "learning generative models using denoising density estimators"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "implicit maximum likelihood estimation"
    - "deep mean shift priors for image restoration"
    - "progressive growing of gans for improved quality stability and variation"
    - "searching for activation functions"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "permutation invariant graph generation via score based generative modeling"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph normalizing flows"
    - "exploring randomly wired neural networks for image recognition"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-5 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "momentum based variance reduction in non convex sgd"
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "aggregated momentum stability through passive damping"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"
    - "regularizing deep networks using efficient layerwise adversarial training"

Connected through hyperedge HE-6 (induced by source paper "injecting hierarchy with u net transformers"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "hierarchical transformers for multi document summarization"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "star transformer"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-7 (induced by source paper "your classifier is secretly an energy based model and you should treat it like one"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "conditional generative models are not robust"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "spectral normalization for generative adversarial networks"
    - "invertible residual networks"
    - "adversarial distillation of bayesian neural network posteriors"

Connected through hyperedge HE-8 (induced by source paper "bridging the gap between f gans and wasserstein gans"):
  with members:
    - "learning implicit generative models with the method of learned moments"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the inductive bias of restricted f gans"
    - "progressive growing of gans for improved quality stability and variation"
    - "approximation and convergence properties of generative adversarial learning"

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)