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: "words are malleable computing semantic shifts in political and media discourse"
  Abstract: Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words. However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions such as social or political variability. We propose an approach for detecting semantic shifts between different viewpoints--broadly defined as a set of texts that share a specific metadata feature, which can be a time-period, but also a social entity such as a political party. For each viewpoint, we learn a semantic space in which each word is represented as a low dimensional neural embedded vector. The challenge is to compare the meaning of a word in one space to its meaning in another space and measure the size of the semantic shifts. We compare the effectiveness of a measure based on optimal transformations between the two spaces with a measure based on the similarity of the neighbors of the word in the respective spaces. Our experiments demonstrate that the combination of these two performs best. We show that the semantic shifts not only occur over time, but also along different viewpoints in a short period of time. For evaluation, we demonstrate how this approach captures meaningful semantic shifts and can help improve other tasks such as the contrastive viewpoint summarization and ideology detection (measured as classification accuracy) in political texts. We also show that the two laws of semantic change which were empirically shown to hold for temporal shifts also hold for shifts across viewpoints. These laws state that frequent words are less likely to shift meaning while words with many senses are more likely to do so.

Connected through hyperedge HE-1 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

Connected through hyperedge HE-2 (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"
    - "dynamic word embeddings for evolving semantic 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"

Connected through hyperedge HE-3 (induced by source paper "generating timelines by modeling semantic change"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "vector embedding of wikipedia concepts and entities"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "timemachine timeline generation for knowledge base entities"
    - "statistically significant detection of linguistic change"
    - "that s sick dude automatic identification of word sense change across different timescales"
    - "timeline generation tracking individuals on twitter"

Connected through hyperedge HE-4 (induced by source paper "using word embeddings to examine gender bias in dutch newspapers 1950 1990"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "deep contextualized word representations"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "quantifying and reducing stereotypes in word embeddings"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-5 (induced by source paper "survey of computational approaches to lexical semantic change"):
  with members:
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "modeling the dynamics of domain specific terminology in diachronic corpora"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "the social dynamics of language change in online networks"

Connected through hyperedge HE-6 (induced by source paper "survey of computational approaches to diachronic conceptual change"):
  with members:
    - "survey of computational approaches to lexical semantic change"
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "dynamic word embeddings for evolving semantic discovery"
    - "diachronic word embeddings and semantic shifts a survey"
    - "on the linearity of semantic change investigating meaning variation via dynamic graph models"
    - "detecting and assessing contextual change in diachronic text documents using context volatility"
    - "the social dynamics of language change in online networks"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"

Connected through hyperedge HE-7 (induced by source paper "diachronic word embeddings and semantic shifts a survey"):
  with members:
    - "dynamic word embeddings for evolving semantic discovery"
    - "incremental skip gram model with negative sampling"
    - "learning word relatedness over time"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "statistically significant detection of linguistic change"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-8 (induced by source paper "short term meaning shift an exploratory distributional analysis"):
  with members:
    - "a state of the art of semantic change computation"
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "diachronic word embeddings and semantic shifts a survey"
    - "competition and selection among conventions"
    - "tracing metaphors in time through self distance in vector spaces"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "temporal analysis of language through neural language models"
    - "distributed representations of words and phrases and their compositionality"

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)