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: "neural autoregressive distribution estimation"
  Abstract: We present Neural Autoregressive Distribution Estimation (NADE) models, which are neural network architectures applied to the problem of unsupervised distribution and density estimation. They leverage the probability product rule and a weight sharing scheme inspired from restricted Boltzmann machines, to yield an estimator that is both tractable and has good generalization performance. We discuss how they achieve competitive performance in modeling both binary and real-valued observations. We also present how deep NADE models can be trained to be agnostic to the ordering of input dimensions used by the autoregressive product rule decomposition. Finally, we also show how to exploit the topological structure of pixels in images using a deep convolutional architecture for NADE.

Connected through hyperedge HE-1 (induced by source paper "a survey on contextual embeddings"):
  with members:
    - "episodic memory in lifelong language learning"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "visualizing and measuring the geometry of bert"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "emerging cross lingual structure in pretrained language models"
    - "bert rediscovers the classical nlp pipeline"
    - "unsupervised cross lingual representation learning at scale"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "noise estimation using density estimation for self supervised multimodal learning"):
  with members:
    - "end to end learning of visual representations from uncurated instructional videos"
    - "use what you have video retrieval using representations from collaborative experts"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "videobert a joint model for video and language representation learning"
    - "grounding object detections with transcriptions"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "learning to detect and retrieve objects from unlabeled videos"

Connected through hyperedge HE-3 (induced by source paper "style compatible object recommendation for multi room indoor scene synthesis"):
  with members:
    - "structured3d a large photo realistic dataset for structured 3d modeling"
    - "fast and scalable position based layout synthesis"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-4 (induced by source paper "xlnet generalized autoregressive pretraining for language understanding"):
  with members:
    - "improving question answering with external knowledge"
    - "option comparison network for multiple choice reading comprehension"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "unsupervised data augmentation for consistency training"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-5 (induced by source paper "video generation from single semantic label map"):
  with members:
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-6 (induced by source paper "moglow probabilistic and controllable motion synthesis using normalising flows"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "analyzing input and output representations for speech driven gesture generation"
    - "generating diverse high fidelity images with vq vae 2"
    - "fixup initialization residual learning without normalization"
    - "videoflow a flow based generative model for video"
    - "waveglow a flow based generative network for speech synthesis"
    - "quaternet a quaternion based recurrent model for human motion"
    - "flowavenet a generative flow for raw audio"

Connected through hyperedge HE-7 (induced by source paper "compression with flows via local bits back coding"):
  with members:
    - "practical lossless compression with latent variables using bits back coding"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "bit swap recursive bits back coding for lossless compression with hierarchical latent variables"
    - "waveglow a flow based generative network for speech synthesis"
    - "invertible residual networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-8 (induced by source paper "density estimation and incremental learning of latent vector for generative autoencoders"):
  with members:
    - "tackling over pruning in variational autoencoders"
    - "vae with a vampprior"
    - "perceptual losses for real time style transfer and super resolution"
    - "density estimation using real nvp"
    - "deep feature consistent variational autoencoder"
    - "wavenet a generative model for raw audio"
    - "made masked autoencoder for distribution estimation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)