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: "isolating sources of disentanglement in variational autoencoders"
  Abstract: We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art $\beta$-VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.

Connected through hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "mass masked sequence to sequence pre training for language generation"
    - "disentangling factors of variation using few labels"
    - "learning to select knowledge for response generation in dialog systems"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised disentanglement without compromises"):
  with members:
    - "disentanglement by nonlinear ica with general incompressible flow networks gin"
    - "weakly supervised disentanglement with guarantees"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "unsupervised model selection for variational disentangled representation learning"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"
    - "causality for machine learning"

Connected through hyperedge HE-3 (induced by source paper "towards a controllable disentanglement network"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "improved training of wasserstein gans"
    - "semantically decomposing the latent spaces of generative adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "high fidelity synthesis with disentangled representation"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "weakly supervised disentanglement by pairwise similarities"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "on the fairness of disentangled representations"
    - "flexibly fair representation learning by disentanglement"

Connected through hyperedge HE-5 (induced by source paper "variational hierarchical dialog autoencoder for dialogue state tracking data augmentation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "dialog state tracking with reinforced data augmentation"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "user modeling for task oriented dialogues"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "a hierarchical latent structure for variational conversation modeling"

Connected through hyperedge HE-6 (induced by source paper "adversarial disentanglement with grouped observations"):
  with members:
    - "disentangling content and style via unsupervised geometry distillation"
    - "disentangling style and content in anime illustrations"
    - "invariant representations without adversarial training"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "exploring disentangled feature representation beyond face identification"
    - "learning latent subspaces in variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "unsupervised adversarial invariance"

Connected through hyperedge HE-7 (induced by source paper "an explicit local and global representation disentanglement framework with applications in deep clustering and unsupervised object detection"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "implicit autoencoders"
    - "explicit disentanglement of appearance and perspective in generative models"
    - "an explicitly relational neural network architecture"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "lstm a search space odyssey"

Connected through hyperedge HE-8 (induced by source paper "semi supervised stylegan for disentanglement learning"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "disentangling factors of variation using few labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)