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: "what regularized auto encoders learn from the data generating distribution"
  Abstract: What do auto-encoders learn about the underlying data generating distribution? Recent work suggests that some auto-encoder variants do a good job of capturing the local manifold structure of data. This paper clarifies some of these previous observations by showing that minimizing a particular form of regularized reconstruction error yields a reconstruction function that locally characterizes the shape of the data generating density. We show that the auto-encoder captures the score (derivative of the log-density with respect to the input). It contradicts previous interpretations of reconstruction error as an energy function. Unlike previous results, the theorems provided here are completely generic and do not depend on the parametrization of the auto-encoder: they show what the auto-encoder would tend to if given enough capacity and examples. These results are for a contractive training criterion we show to be similar to the denoising auto-encoder training criterion with small corruption noise, but with contraction applied on the whole reconstruction function rather than just encoder. Similarly to score matching, one can consider the proposed training criterion as a convenient alternative to maximum likelihood because it does not involve a partition function. Finally, we show how an approximate Metropolis-Hastings MCMC can be setup to recover samples from the estimated distribution, and this is confirmed in sampling experiments.

Connected through hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis"):
  with members:
    - "session based recommendation with graph neural network"
    - "contextual hybrid session based news recommendation with recurrent neural networks"
    - "on the importance of news content representation in hybrid neural session based recommender systems"
    - "deep learning based recommender system a survey and new perspectives"
    - "neurec on nonlinear transformation for personalized ranking"
    - "sequence aware recommender systems"
    - "evaluation of session based recommendation algorithms"
    - "multi pointer co attention networks for recommendation"

Connected through hyperedge HE-2 (induced by source paper "learning generative models using denoising density estimators"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multi source deep gaussian process kernel learning"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "interpretable deep gaussian processes with moments"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "deep multi fidelity gaussian processes"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "deep kernel learning"
    - "generalized denoising auto encoders as generative models"
    - "gaussian processes for big data"

Connected through hyperedge HE-4 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-5 (induced by source paper "generating classification weights with gnn denoising autoencoders for few shot learning"):
  with members:
    - "meta learning for semi supervised few shot classification"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-6 (induced by source paper "generative latent flow"):
  with members:
    - "diagnosing and enhancing vae models"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "implicit maximum likelihood estimation"
    - "progressive growing of gans for improved quality stability and variation"
    - "learnable explicit density for continuous latent space and variational inference"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a deep optimization approach for image deconvolution"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning an optimizer for image deconvolution"
    - "scale recurrent network for deep image deblurring"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "learning a discriminative prior for blind image deblurring"
    - "blind image deconvolution using deep generative priors"
    - "learning collaborative generation correction modules for blind image deblurring and beyond"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-8 (induced by source paper "coopsubnet cooperating subnetwork for data driven regularization of deep networks under limited training budgets"):
  with members:
    - "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"
    - "regularization for deep learning a taxonomy"
    - "deepssm a deep learning framework for statistical shape modeling from raw images"
    - "autoaugment learning augmentation policies from data"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "end to end representation learning for correlation filter based tracking"
    - "ldmnet low dimensional manifold regularized neural networks"
    - "learning a predictable and generative vector representation for objects"

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)