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: "multimodal generative models for scalable weakly supervised learning"
  Abstract: Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous work have proposed generative models to handle multi-modal input. However, these models either do not learn a joint distribution or require complex additional computations to handle missing data. Here, we introduce a multimodal variational autoencoder that uses a product-of-experts inference network and a sub-sampled training paradigm to solve the multi-modal inference problem. Notably, our model shares parameters to efficiently learn under any combination of missing modalities, thereby enabling weakly-supervised learning. We apply our method on four datasets and show that we match state-of-the-art performance using many fewer parameters. In each case our approach yields strong weakly-supervised results. We then consider a case study of learning image transformations---edge detection, colorization, facial landmark segmentation, etc.---as a set of modalities. We find appealing results across this range of tasks.

Connected through hyperedge HE-1 (induced by source paper "variational item response theory fast accurate and expressive"):
  with members:
    - "the stanford acuity test a probabilistic approach for precise visual acuity testing"
    - "deep irt make deep learning based knowledge tracing explainable using item response theory"
    - "neural autoregressive flows"
    - "pyro deep universal probabilistic programming"
    - "attention is all you need"
    - "improving variational auto encoders using householder flow"
    - "dynamic key value memory networks for knowledge tracing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"

Connected through hyperedge HE-4 (induced by source paper "a multi task approach for disentangling syntax and semantics in sentence representations"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "disentangled representation learning for non parallel text style transfer"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "the omg emotion behavior dataset"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "pomegranate fast and flexible probabilistic modeling in python"

Connected through hyperedge HE-6 (induced by source paper "neural multisensory scene inference"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "attentive neural processes"
    - "consistent generative query networks"
    - "federated learning strategies for improving communication efficiency"
    - "conditional neural processes"
    - "learning models for visual 3d localization with implicit mapping"
    - "pixels voxels and views a study of shape representations for single view 3d object shape prediction"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-7 (induced by source paper "cross modal variational auto encoder with distributed latent spaces and associators"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "learning to localize sound source in visual scenes"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "cross modal deep variational hand pose estimation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "conditional generation of multi modal data using constrained embedding space mapping"
    - "multimodal machine learning a survey and taxonomy"

Connected through hyperedge HE-8 (induced by source paper "generative grading neural approximate parsing for automated student feedback"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "reliable deep grade prediction with uncertainty estimation"
    - "automated essay scoring based on two stage learning"
    - "training classifiers with natural language explanations"
    - "zero shot learning for code education rubric sampling with deep learning inference"
    - "attention is all you need"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "inference compilation and universal probabilistic programming"

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)