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: "structured embedding models for grouped data"
  Abstract: Word embeddings are a powerful approach for analyzing language, and exponential family embeddings (EFE) extend them to other types of data. Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data. We study how the word usage of U.S. Congressional speeches varies across states and party affiliation, how words are used differently across sections of the ArXiv, and how the co-purchase patterns of groceries can vary across seasons. Key to the success of our method is that the groups share statistical information. We develop two sharing strategies: hierarchical modeling and amortization. We demonstrate the benefits of this approach in empirical studies of speeches, abstracts, and shopping baskets. We show how S-EFE enables group-specific interpretation of word usage, and outperforms EFE in predicting held-out data.

Connected through hyperedge HE-1 (induced by source paper "scalable bundling via dense product embeddings"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "learning dynamic author representations with temporal language models"):
  with members:
    - "unsupervised text style transfer using language models as discriminators"
    - "multiple attribute text style transfer"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "construction of the literature graph in semantic scholar"
    - "dynamic word embeddings for evolving semantic discovery"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "exponential family graph embeddings"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "kernel node embeddings"
    - "a comprehensive survey on graph neural networks"
    - "tne a latent model for representation learning on networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "biasedwalk biased sampling for representation learning on graphs"
    - "verse versatile graph embeddings from similarity measures"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic framework for learning domain specific hierarchical word embeddings"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "neural collaborative filtering"
    - "learned in translation contextualized word vectors"
    - "a mixture model for learning multi sense word embeddings"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-5 (induced by source paper "word embeddings quantify 100 years of gender and ethnic stereotypes"):
  with members:
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "stereotyping and bias in the flickr30k dataset"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "statistically significant detection of linguistic change"
    - "efficient estimation of word representations in vector space"

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)