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: "hyperprior induced unsupervised disentanglement of latent representations"
  Abstract: We address the problem of unsupervised disentanglement of latent representations learnt via deep generative models. In contrast to current approaches that operate on the evidence lower bound (ELBO), we argue that statistical independence in the latent space of VAEs can be enforced in a principled hierarchical Bayesian manner. To this effect, we augment the standard VAE with an inverse-Wishart (IW) prior on the covariance matrix of the latent code. By tuning the IW parameters, we are able to encourage (or discourage) independence in the learnt latent dimensions. Extensive experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and CelebA) show our approach to outperform the $\beta$-VAE and is competitive with the state-of-the-art FactorVAE. Our approach achieves significantly better disentanglement and reconstruction on a new dataset (CorrelatedEllipses) which introduces correlations between the factors of variation.

Connected through hyperedge HE-1 (induced by source paper "geometric disentanglement for generative latent shape models"):
  with members:
    - "shape retrieval of non rigid 3d human models"
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "a simple probabilistic deep generative model for learning generalizable disentangled representations from grouped data"
    - "point convolutional neural networks by extension operators"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "infogan cr disentangling generative adversarial networks with contrastive regularizers"):
  with members:
    - "infocatvae representation learning with categorical variational autoencoders"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "disentangled vae representations for multi aspect and missing data"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised disentangled representation learning with analogical relations"
    - "auto encoding total correlation explanation"

Connected through hyperedge HE-3 (induced by source paper "embedding symbolic knowledge into deep networks"):
  with members:
    - "learning logistic circuits"
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "logic tensor networks for semantic image interpretation"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"
    - "learning continuous semantic representations of symbolic expressions"

Connected through hyperedge HE-4 (induced by source paper "semantically regularized logic graph embeddings"):
  with members:
    - "can neural networks understand logical entailment"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "a survey on deep learning in medical image analysis"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "harnessing deep neural networks with logic rules"
    - "learning continuous semantic representations of symbolic expressions"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "visual relationship detection with language priors"

Connected through hyperedge HE-5 (induced by source paper "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"):
  with members:
    - "relevance factor vae learning and identifying disentangled factors"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "towards a definition of disentangled representations"
    - "vae with a vampprior"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "deep learning face attributes in the wild"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "on controlled deentanglement for natural language processing"):
  with members:
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "making the v in vqa matter elevating the role of image understanding in visual question answering"

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)