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: "generative models of visually grounded imagination"
  Abstract: It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we show how we can modify variational auto-encoders to perform this task. Our method uses a novel training objective, and a novel product-of-experts inference network, which can handle partially specified (abstract) concepts in a principled and efficient way. We also propose a set of easy-to-compute evaluation metrics that capture our intuitive notions of what it means to have good visual imagination, namely correctness, coverage, and compositionality (the 3 C's). Finally, we perform a detailed comparison of our method with two existing joint image-attribute VAE methods (the JMVAE method of Suzuki this http URL. and the BiVCCA method of Wang this http URL.) by applying them to two datasets: the MNIST-with-attributes dataset (which we introduce here), and the CelebA dataset.

Connected through hyperedge HE-1 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "probabilistic neural symbolic models for interpretable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evalai towards better evaluation systems for ai agents"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "houdini lifelong learning as program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"

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 "emergence of compositional language with deep generational transmission"):
  with members:
    - "compositional obverter communication learning from raw visual input"
    - "translating neuralese"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "large scale evolution of image classifiers"
    - "don t just assume look and answer overcoming priors for visual question answering"
    - "natural language does not emerge naturally in multi agent dialog"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a prior of a googol gaussians a tensor ring induced prior for generative models"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "spectral normalization for generative adversarial networks"
    - "latent dirichlet allocation in generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"

Connected through hyperedge HE-6 (induced by source paper "a hierarchical grocery store image dataset with visual and semantic labels"):
  with members:
    - "advances in variational inference"
    - "deep variational canonical correlation analysis"
    - "fruit recognition from images using deep learning"
    - "densely connected convolutional networks"
    - "inter battery topic representation learning"
    - "deep fruit detection in orchards"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-7 (induced by source paper "a generative map for image based camera localization"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "learning models for visual 3d localization with implicit mapping"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "deep variational canonical correlation analysis"
    - "image based localization using hourglass networks"
    - "geometry aware learning of maps for camera localization"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-8 (induced by source paper "imitation learning of factored multi agent reactive models"):
  with members:
    - "generating multi agent trajectories using programmatic weak supervision"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "fixing a broken elbo"
    - "generative temporal models with memory"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "unsupervised perceptual rewards for imitation learning"

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)