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: "bayesian neural word embedding"
  Abstract: Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram (SG) with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm that can be beneficial to general item similarity tasks as well. The algorithm relies on a Variational Bayes solution for the SG objective and a detailed step by step description of the algorithm is provided. We present experimental results that demonstrate the performance of the proposed algorithm and show it is competitive with the original SG method.

Connected through hyperedge HE-1 (induced by source paper "attentive item2vec neural attentive user representations"):
  with members:
    - "neural attentive multiview machines"
    - "scalable attentive sentence pair modeling via distilled sentence embedding"
    - "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "neural attentive multiview machines"):
  with members:
    - "attentive item2vec neural attentive user representations"
    - "scalable attentive sentence pair modeling via distilled sentence embedding"
    - "multiscale self attentive convolutions for vision and language modeling"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multimodal machine learning a survey and taxonomy"
    - "gaussian process regression for out of sample extension"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "correlation coefficients and semantic textual similarity"):
  with members:
    - "don t settle for average go for the max fuzzy sets and max pooled word vectors"
    - "deep contextualized word representations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "a rank based similarity metric for word embeddings"
    - "decoding decoders finding optimal representation spaces for unsupervised similarity tasks"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-4 (induced by source paper "scalable attentive sentence pair modeling via distilled sentence embedding"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "hierarchical neural language models for joint representation of streaming documents and their content"

Connected through hyperedge HE-6 (induced by source paper "inversynth deep estimation of synthesizer parameter configurations from audio signals"):
  with members:
    - "neural network gradient based learning of black box function interfaces"
    - "deep synthesizer parameter estimation"
    - "exposure a white box photo post processing framework"
    - "automatic tagging using deep convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "interpretable word embeddings via informative priors"):
  with members:
    - "identification interpretability and bayesian word embeddings"
    - "analytical methods for interpretable ultradense word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "sparse overcomplete word vector representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "neural variational inference for estimating uncertainty in knowledge graph embeddings"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "variational knowledge graph reasoning"
    - "deep bayesian active learning with image data"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "neural variational inference for text processing"

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.CL(Computation and Language)