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: "disentangling space and time in video with hierarchical variational auto encoders"
  Abstract: There are many forms of feature information present in video data. Principle among them are object identity information which is largely static across multiple video frames, and object pose and style information which continuously transforms from frame to frame. Most existing models confound these two types of representation by mapping them to a shared feature space. In this paper we propose a probabilistic approach for learning separable representations of object identity and pose information using unsupervised video data. Our approach leverages a deep generative model with a factored prior distribution that encodes properties of temporal invariances in the hidden feature set. Learning is achieved via variational inference. We present results of learning identity and pose information on a dataset of moving characters as well as a dataset of rotating 3D objects. Our experimental results demonstrate our model's success in factoring its representation, and demonstrate that the model achieves improved performance in transfer learning tasks.

Connected through hyperedge HE-1 (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"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "recent advances in autoencoder based representation learning"

Connected through hyperedge HE-2 (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"
    - "disentangled sequential autoencoder"
    - "gaussian process prior variational autoencoders"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-3 (induced by source paper "disentangling video with independent prediction"):
  with members:
    - "self normalizing neural networks"
    - "independently controllable factors"
    - "unsupervised learning of disentangled representations from video"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "understanding visual concepts with continuation learning"
    - "a compositional object based approach to learning physical dynamics"
    - "unsupervised learning of video representations using lstms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "isolating sources of disentanglement in variational autoencoders"):
  with members:
    - "auto encoding total correlation explanation"
    - "mine mutual information neural estimation"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "independently controllable factors"
    - "infovae information maximizing variational autoencoders"
    - "emergence of invariance and disentanglement in deep representations"
    - "fixing a broken elbo"
    - "on the emergence of invariance and disentangling in deep representations"

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.CV(Computer Vision and Pattern Recognition)