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: "word embeddings quantify 100 years of gender and ethnic stereotypes"
  Abstract: Word embeddings are a powerful machine-learning framework that represents each English word by a vector. The geometric relationship between these vectors captures meaningful semantic relationships between the corresponding words. In this paper, we develop a framework to demonstrate how the temporal dynamics of the embedding helps to quantify changes in stereotypes and attitudes toward women and ethnic minorities in the 20th and 21st centuries in the United States. We integrate word embeddings trained on 100 y of text data with the US Census to show that changes in the embedding track closely with demographic and occupation shifts over time. The embedding captures societal shifts—e.g., the women’s movement in the 1960s and Asian immigration into the United States—and also illuminates how specific adjectives and occupations became more closely associated with certain populations over time. Our framework for temporal analysis of word embedding opens up a fruitful intersection between machine learning and quantitative social science.

Connected through hyperedge HE-1 (induced by source paper "think locally act globally federated learning with local and global representations"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "improved adversarial learning for fair classification"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "a framework for the computational linguistic analysis of dehumanization"):
  with members:
    - "social bias frames reasoning about social and power implications of language"
    - "mitigating gender bias in natural language processing literature review"
    - "talkdown a corpus for condescension detection in context"
    - "automatically neutralizing subjective bias in text"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "entity centric contextual affective analysis"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-3 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition"):
  with members:
    - "mitigating gender bias in natural language processing literature review"
    - "huggingface s transformers state of the art natural language processing"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "how to fine tune bert for text classification"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "counterfactual fairness in text classification through robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "no computation without representation avoiding data and algorithm biases through diversity"):
  with members:
    - "problem formulation and fairness"
    - "efficient search for diverse coherent explanations"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "crowdsourcing with fairness diversity and budget constraints"
    - "the social cost of strategic classification"
    - "fairness through causal awareness learning latent variable models for biased data"
    - "creating fair models of atherosclerotic cardiovascular disease risk"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"

Connected through hyperedge HE-5 (induced by source paper "neural embeddings of scholarly periodicals reveal complex disciplinary organizations"):
  with members:
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "the preeminence of ethnic diversity in scientific collaboration"
    - "semaxis a lightweight framework to characterize domain specific word semantics beyond sentiment"
    - "a style based generator architecture for generative adversarial networks"
    - "semantics derived automatically from language corpora contain human like biases"
    - "node2vec scalable feature learning for networks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-6 (induced by source paper "where new words are born distributional semantic analysis of neologisms and their semantic neighborhoods"):
  with members:
    - "survey of computational approaches to diachronic conceptual change"
    - "making fetch happen the influence of social and linguistic context on nonstandard word growth and decline"
    - "the road to success assessing the fate of linguistic innovations in online communities"
    - "diachronic word embeddings and semantic shifts a survey"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "statistically significant detection of linguistic change"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-7 (induced by source paper "social centralization and semantic collapse hyperbolic embeddings of networks and text"):
  with members:
    - "the wisdom of polarized crowds"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "poincar e embeddings for learning hierarchical representations"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "network mapping by replaying hyperbolic growth"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "echoes of power language effects and power differences in social interaction"

Connected through hyperedge HE-8 (induced by source paper "revealing neural network bias to non experts through interactive counterfactual examples"):
  with members:
    - "explaining vulnerabilities to adversarial machine learning through visual analytics"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "learning not to learn training deep neural networks with biased data"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "why should i trust you explaining the predictions of any classifier"

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)