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: "the geometry of culture analyzing the meanings of class through word embeddings"
  Abstract: We argue word embedding models are a useful tool for the study of culture using a historical analysis of shared understandings of social class as an empirical case. Word embeddings represent semant...

Connected through hyperedge HE-1 (induced by source paper "neural embeddings of scholarly periodicals reveal complex disciplinary organizations"):
  with members:
    - "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"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "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-2 (induced by source paper "wide range screening of algorithmic bias in word embedding models using large sentiment lexicons reveals underreported bias types"):
  with members:
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "algorithmic decision making and the cost of fairness"
    - "semantics derived automatically from language corpora contain human like biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "frameaxis characterizing framing bias and intensity with word embedding"):
  with members:
    - "semaxis a lightweight framework to characterize domain specific word semantics beyond sentiment"
    - "framing and agenda setting in russian news a computational analysis of intricate political strategies"
    - "gendered conversation in a social game streaming platform"
    - "distributed representations of words and phrases and their compositionality"
    - "measuring the happiness of large scale written expression songs blogs and presidents"

Connected through hyperedge HE-4 (induced by source paper "predictive biases in natural language processing models a conceptual framework and overview"):
  with members:
    - "what s in a name reducing bias in bios without access to protected attributes"
    - "evaluating gender bias in machine translation"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "a framework for understanding unintended consequences of machine learning"
    - "measuring bias in contextualized word representations"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"

Connected through hyperedge HE-5 (induced by source paper "fair is better than sensational man is to doctor as woman is to doctor"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "deep contextualized word representations"
    - "datasheets for datasets"

Connected through hyperedge HE-6 (induced by source paper "tracing antisemitic language through diachronic embedding projections france 1789 1914"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "temporal dynamics of semantic relations in word embeddings an application to predicting armed conflict participants"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "cultural shift or linguistic drift comparing two computational measures of semantic change"
    - "diachronic word embeddings reveal statistical laws of semantic change"

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"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "bayesian neural word embedding"
    - "sparse overcomplete word vector representations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "humor in word embeddings cockamamie gobbledegook for nincompoops"):
  with members:
    - "reverse engineering satire or paper on computational humor accepted despite making serious advances"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "advances in pre training distributed word representations"
    - "semantics derived automatically from language corpora contain human like biases"
    - "are word embedding based features useful for sarcasm detection"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "humor in collective discourse unsupervised funniness detection in the new yorker cartoon caption contest"

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)