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: "dynamic word embeddings for evolving semantic discovery"
  Abstract: Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting alignment problem. This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.

Connected through hyperedge HE-1 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis"):
  with members:
    - "from web crawled text to project descriptions automatic summarizing of social innovation projects"
    - "an analysis of hierarchical text classification using word embeddings"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "character n gram embeddings to improve rnn language models"
    - "word class embeddings for multiclass text classification"
    - "using text analysis to quantify the similarity and evolution of scientific disciplines"
    - "sentiment classification of customer s reviews about automobiles in roman urdu"
    - "training of deep neural networks based on distance measures using rmsprop"

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"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-3 (induced by source paper "dynamic network embeddings for network evolution analysis"):
  with members:
    - "dyngem deep embedding method for dynamic graphs"
    - "diachronic word embeddings and semantic shifts a survey"
    - "struc2vec learning node representations from structural identity"
    - "motifs in temporal networks"
    - "timers error bounded svd restart on dynamic networks"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "adversarial network embedding"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"

Connected through hyperedge HE-4 (induced by source paper "follow the leader documents on the leading edge of semantic change get more citations"):
  with members:
    - "survey of computational approaches to diachronic conceptual change"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "diachronic word embeddings and semantic shifts a survey"
    - "bounded rationality in scholarly knowledge discovery"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "temporal analysis of language through neural language models"

Connected through hyperedge HE-5 (induced by source paper "time out temporal referencing for robust modeling of lexical semantic change"):
  with members:
    - "gasc genre aware semantic change for ancient greek"
    - "survey of computational approaches to lexical semantic change"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "german in flux detecting metaphoric change via word entropy"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-6 (induced by source paper "back to the future sequential alignment of text representations"):
  with members:
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "a review of domain adaptation without target labels"
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "rumoureval 2019 determining rumour veracity and support for rumours"
    - "diachronic word embeddings and semantic shifts a survey"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "training temporal word embeddings with a compass"):
  with members:
    - "tracking the evolution of words with time reflective text representations"
    - "learning distributed representations of texts and entities from knowledge base"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "semantics derived automatically from language corpora contain human like biases"
    - "tracing metaphors in time through self distance in vector spaces"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "document classification by inversion of distributed language representations"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-8 (induced by source paper "an improved historical embedding without alignment"):
  with members:
    - "deep contextualized word representations"
    - "tracking the evolution of words with time reflective text representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "words are malleable computing semantic shifts in political and media discourse"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"

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)