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: "a deep and autoregressive approach for topic modeling of multimodal data"
  Abstract: Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

Connected through hyperedge HE-1 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "shakeout a new approach to regularized deep neural network training"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "sparse activity and sparse connectivity in supervised learning"
    - "improved dropout for shallow and deep learning"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-3 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification"):
  with members:
    - "deep image super resolution via natural image priors"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "dynamic routing between capsules"
    - "discriminative embeddings of latent variable models for structured data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "neural variational inference for text processing"
    - "spatial transformer networks"
    - "end to end learning of lda by mirror descent back propagation over a deep architecture"

Connected through hyperedge HE-4 (induced by source paper "lifelong neural topic learning in contextualized autoregressive topic models of language via informative transfers"):
  with members:
    - "improving topic models with latent feature word representations"
    - "lisa explaining recurrent neural network judgments via layer wise semantic accumulation and example to pattern transformation"
    - "variational inference in pachinko allocation machines"
    - "replicated siamese lstm in ticketing system for similarity learning and retrieval in asymmetric texts"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "document neural autoregressive distribution estimation"
    - "distributed representations of sentences and documents"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-5 (induced by source paper "texttovec deep contextualized neural autoregressive topic models of language with distributed compositional prior"):
  with members:
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "deep temporal recurrent replicated softmax for topical trends over time"
    - "topically driven neural language model"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "document neural autoregressive distribution estimation"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "document informed neural autoregressive topic models with distributional prior"):
  with members:
    - "improving topic models with latent feature word representations"
    - "document neural autoregressive distribution estimation"
    - "combining recurrent and convolutional neural networks for relation classification"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "deepfm a factorization machine based neural network for ctr prediction"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "sequential click prediction for sponsored search with recurrent neural networks"
    - "collaborative deep learning for recommender systems"

Connected through hyperedge HE-8 (induced by source paper "variational deep embedding an unsupervised and generative approach to clustering"):
  with members:
    - "generating images with perceptual similarity metrics based on deep networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "improved techniques for training gans"
    - "a neural autoregressive approach to collaborative filtering"
    - "pixel recurrent neural networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "infinite variational autoencoder for semi supervised learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)