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: "from variational to deterministic autoencoders"
  Abstract: Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce "blurry" images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

Connected through hyperedge HE-1 (induced by source paper "on implicit regularization in beta vaes"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "explaining explanations an overview of interpretability of machine learning"
    - "variational autoencoders pursue pca directions by accident"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "fixing a broken elbo"

Connected through hyperedge HE-2 (induced by source paper "deterministic decoding for discrete data in variational autoencoders"):
  with members:
    - "a prior of a googol gaussians a tensor ring induced prior for generative models"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "junction tree variational autoencoder for molecular graph generation"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "vae with a vampprior"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-3 (induced by source paper "regularized autoencoders via relaxed injective probability flow"):
  with members:
    - "understanding the un interpretability of natural image distributions using generative models"
    - "diagnosing and enhancing vae models"
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "neural discrete representation learning"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "batch norm with entropic regularization turns deterministic autoencoders into generative models"):
  with members:
    - "online normalization for training neural networks"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "batch normalization is a cause of adversarial vulnerability"
    - "fixup initialization residual learning without normalization"
    - "implicit maximum likelihood estimation"
    - "diverse image synthesis from semantic layouts via conditional imle"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-5 (induced by source paper "balancing reconstruction error and kullback leibler divergence in variational autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "sparsity in variational autoencoders"
    - "tackling over pruning in variational autoencoders"
    - "fixing a broken elbo"
    - "vae with a vampprior"
    - "improving variational inference with inverse autoregressive flow"
    - "importance weighted autoencoders"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-6 (induced by source paper "variance loss in variational autoencoders"):
  with members:
    - "diagnosing and enhancing vae models"
    - "sparsity in variational autoencoders"
    - "vae with a vampprior"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "importance weighted autoencoders"

Connected through hyperedge HE-7 (induced by source paper "dressing 3d humans using a conditional mesh vae gan"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "meshgan non linear 3d morphable models of faces"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "facsimile fast and accurate scans from an image in less than a second"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "deephuman 3d human reconstruction from a single image"
    - "multi garment net learning to dress 3d people from images"

Connected through hyperedge HE-8 (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"
    - "diversity sensitive conditional generative adversarial networks"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"

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)