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 factors of variation with cycle consistent variational auto encoders"
  Abstract: Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training.

Connected through hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "diversity in faces"
    - "toward gender inclusive coreference resolution"
    - "robust ordinal vae employing noisy pairwise comparisons for disentanglement"
    - "weakly supervised disentanglement with guarantees"
    - "contrastive variational autoencoder enhances salient features"
    - "weakly supervised disentanglement by pairwise similarities"

Connected through hyperedge HE-2 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"

Connected through hyperedge HE-3 (induced by source paper "zero shot imitation learning from demonstrations for legged robot visual navigation"):
  with members:
    - "long range indoor navigation with prm rl"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "learning navigation subroutines from egocentric videos"
    - "universal planning networks"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "zero shot visual imitation"

Connected through hyperedge HE-4 (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"
    - "geometry of deep generative models for disentangled representations"
    - "disentangled representation learning for non parallel text style transfer"
    - "mr gan manifold regularized generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "mggan solving mode collapse using manifold guided training"

Connected through hyperedge HE-5 (induced by source paper "demystifying inter class disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "generating diverse high fidelity images with vq vae 2"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "nam non adversarial unsupervised domain mapping"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "a cyclically trained adversarial network for invariant representation learning"):
  with members:
    - "a two step disentanglement method"
    - "domain generalization via conditional invariant representation"
    - "vgan based image representation learning for privacy preserving facial expression recognition"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "controllable invariance through adversarial feature learning"
    - "fader networks manipulating images by sliding attributes"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "theory and evaluation metrics for learning disentangled representations"):
  with members:
    - "learning deep disentangled embeddings with the f statistic loss"
    - "isolating sources of disentanglement in variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "towards a definition of disentangled representations"
    - "variational autoencoders pursue pca directions by accident"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "deep variational information bottleneck"

Connected through hyperedge HE-8 (induced by source paper "cross domain image manipulation by demonstration"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "training deep face recognition systems with synthetic data"
    - "neural best buddies sparse cross domain correspondence"
    - "multimodal unsupervised image to image translation"
    - "dida disentangled synthesis for domain adaptation"
    - "isolating sources of disentanglement in variational autoencoders"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"

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)