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: "geometry of deep generative models for disentangled representations"
  Abstract: Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an embedding function. The geometric properties of these latent spaces has been studied under the lens of Riemannian geometry; via analysis of the non-linearity of the generator function. In new developments, deep generative models have been used for learning semantically meaningful `disentangled' representations; that capture task relevant attributes while being invariant to other attributes. In this work, we explore the geometry of popular generative models for disentangled representation learning. We use several metrics to compare the properties of latent spaces of disentangled representation models in terms of class separability and curvature of the latent-space. The results we obtain establish that the class distinguishable features in the disentangled latent space exhibits higher curvature as opposed to a variational autoencoder. We evaluate and compare the geometry of three such models with variational autoencoder on two different datasets. Further, our results show that distances and interpolation in the latent space are significantly improved with Riemannian metrics derived from the curvature of the space. We expect these results will have implications on understanding how deep-networks can be made more robust, generalizable, as well as interpretable.

Connected through hyperedge HE-1 (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"
    - "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"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-2 (induced by source paper "product of orthogonal spheres parameterization for disentangled representation learning"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling pose from appearance in monochrome hand images"
    - "disentangled representation learning for non parallel text style transfer"
    - "mr gan manifold regularized generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"
    - "mggan solving mode collapse using manifold guided training"

Connected through hyperedge HE-3 (induced by source paper "dimensionality compression and expansion in deep neural networks"):
  with members:
    - "the local dimension of deep manifold"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "understanding convolutional neural networks with information theory an initial exploration"
    - "fisher rao metric geometry and complexity of neural networks"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "the riemannian geometry of deep generative models"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"

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)