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: "disentangled sequential autoencoder"
  Abstract: We present a VAE architecture for encoding and generating high dimensional sequential data, such as video or audio. Our deep generative model learns a latent representation of the data which is split into a static and dynamic part, allowing us to approximately disentangle latent time-dependent features (dynamics) from features which are preserved over time (content). This architecture gives us partial control over generating content and dynamics by conditioning on either one of these sets of features. In our experiments on artificially generated cartoon video clips and voice recordings, we show that we can convert the content of a given sequence into another one by such content swapping. For audio, this allows us to convert a male speaker into a female speaker and vice versa, while for video we can separately manipulate shapes and dynamics. Furthermore, we give empirical evidence for the hypothesis that stochastic RNNs as latent state models are more efficient at compressing and generating long sequences than deterministic ones, which may be relevant for applications in video compression.

Connected through hyperedge HE-1 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "learning latent dynamics for planning from pixels"

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 "disentangling representations using gaussian processes in variational autoencoders for video prediction"):
  with members:
    - "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"
    - "geometry of deep generative models for disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "gaussian process prior variational autoencoders"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-4 (induced by source paper "variational inference formulation for a model free simulation of a dynamical system with unknown parameters by a recurrent neural network"):
  with members:
    - "short note on the behavior of recurrent neural network for noisy dynamical system"
    - "learning functionals via lstm neural networks for predicting vessel dynamics in extreme sea states"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "sgdr stochastic gradient descent with restarts"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "are disentangled representations helpful for abstract visual reasoning"):
  with members:
    - "learning to make analogies by contrasting abstract relational structure"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling factors of variation using few labels"
    - "on the fairness of disentangled representations"
    - "multi object representation learning with iterative variational inference"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "competitive training of mixtures of independent deep generative models"
    - "learning deep disentangled embeddings with the f statistic loss"

Connected through hyperedge HE-6 (induced by source paper "on the fairness of disentangled representations"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on variational bounds of mutual information"
    - "competitive training of mixtures of independent deep generative models"
    - "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"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "unsupervised speech representation learning using wavenet autoencoders"):
  with members:
    - "the zero resource speech challenge 2019 tts without t"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised machine translation using monolingual corpora only"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a segmental framework for fully unsupervised large vocabulary speech recognition"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-8 (induced by source paper "disentangling factors of variation using few labels"):
  with members:
    - "are disentangled representations helpful for abstract visual reasoning"
    - "learning disentangled representations with reference based variational autoencoders"
    - "on the fairness of disentangled representations"
    - "competitive training of mixtures of independent deep generative models"
    - "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"
    - "isolating sources of disentanglement in variational autoencoders"

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)