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: "variational autoencoders pursue pca directions by accident"
  Abstract: The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.

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"
    - "from variational to deterministic autoencoders"
    - "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 inference of disentangled latent concepts from unlabeled observations"
    - "fixing a broken elbo"

Connected through hyperedge HE-2 (induced by source paper "rate distortion optimization guided autoencoder for isometric embedding in euclidean latent space"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "fixing a broken elbo"
    - "the riemannian geometry of deep generative models"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "representation learning through latent canonicalizations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a closed form solution to photorealistic image stylization"
    - "yolov3 an incremental improvement"
    - "semantic aware grad gan for virtual to real urban scene adaption"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "unsupervised model selection for variational disentangled representation learning"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "on the fairness of disentangled representations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-5 (induced by source paper "the usual suspects reassessing blame for vae posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "improving explorability in variational inference with annealed variational objectives"
    - "tackling over pruning in variational autoencoders"
    - "hidden talents of the variational autoencoder"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-6 (induced by source paper "don t blame the elbo a linear vae perspective on posterior collapse"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "preventing posterior collapse with delta vaes"
    - "loss landscapes of regularized linear autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "inference suboptimality in variational autoencoders"
    - "improving explorability in variational inference with annealed variational objectives"

Connected through hyperedge HE-7 (induced by source paper "theory and evaluation metrics for learning disentangled representations"):
  with members:
    - "learning deep disentangled embeddings with the f statistic loss"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "recent advances in autoencoder based representation learning"
    - "towards a definition of disentangled representations"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "deep variational information bottleneck"

Connected through hyperedge HE-8 (induced by source paper "rate distortion optimization guided autoencoder for generative approach with quantitatively measurable latent space"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "neural discrete representation learning"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "density modeling of images using a generalized normalization transformation"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"

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)