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: "auxiliary guided autoregressive variational autoencoders"
  Abstract: Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models combining the strengths of both models. Our contribution is to train such hybrid models using an auxiliary loss function that controls which information is captured by the latent variables and what is left to the autoregressive decoder. In contrast, prior work on such hybrid models needed to limit the capacity of the autoregressive decoder to prevent degenerate models that ignore the latent variables and only rely on autoregressive modeling. Our approach results in models with meaningful latent variable representations, and which rely on powerful autoregressive decoders to model image details. Our model generates qualitatively convincing samples, and yields state-of-the-art quantitative results.

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 "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-3 (induced by source paper "dueling decoders regularizing variational autoencoder latent spaces"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning latent dynamics for planning from pixels"
    - "therml the thermodynamics of machine learning"
    - "group normalization"
    - "uncertainty in the variational information bottleneck"
    - "the conditional entropy bottleneck"
    - "cross modal deep variational hand pose estimation"
    - "β vaes can retain label information even at high compression"

Connected through hyperedge HE-4 (induced by source paper "preventing posterior collapse with delta vaes"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "stochastic video generation with a learned prior"
    - "autoregressive quantile networks for generative modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "theory and experiments on vector quantized autoencoders"
    - "associative compression networks for representation learning"
    - "generating sentences by editing prototypes"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "adversarial training of partially invertible variational autoencoders"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "modulating early visual processing by language"
    - "it takes only two adversarial generator encoder networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "ρ vae autoregressive parametrization of the vae encoder"):
  with members:
    - "diagnosing and enhancing vae models"
    - "recent advances in autoencoder based representation learning"
    - "infovae information maximizing variational autoencoders"
    - "vae with a vampprior"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "pixelvae a latent variable model for natural images"
    - "variational lossy autoencoder"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "using neural networks for programming by demonstration"):
  with members:
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "safe remote access swarm robotics research on the robotarium"
    - "learning to learn by gradient descent by gradient descent"

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)