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: "evaluating the underlying gender bias in contextualized word embeddings"
  Abstract: Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. #R##N#In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.

Connected through hyperedge HE-1 (induced by source paper "reducing sentiment bias in language models via counterfactual evaluation"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "certified robustness to adversarial word substitutions"
    - "release strategies and the social impacts of language models"
    - "multi task deep neural networks for natural language understanding"
    - "reducing gender bias in word level language models with a gender equalizing loss function"
    - "transformer xl attentive language models beyond a fixed length context"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-2 (induced by source paper "assessing social and intersectional biases in contextualized word representations"):
  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"
    - "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"
    - "understanding the origins of bias in word embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "measuring social bias in knowledge graph embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluating gender bias in machine translation"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "ernie enhanced language representation with informative entities"
    - "compositional fairness constraints for graph embeddings"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-4 (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"
    - "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"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "deep contextualized word representations"
    - "datasheets for datasets"

Connected through hyperedge HE-5 (induced by source paper "measuring bias in contextualized word representations"):
  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"
    - "gender bias in contextualized word embeddings"
    - "entity centric contextual affective analysis"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "gender bias in coreference resolution"
    - "feature wise bias amplification"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "examining gender bias in languages with grammatical gender"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "attenuating bias in word vectors"
    - "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"
    - "identifying and reducing gender bias in word level language models"
    - "understanding undesirable word embedding associations"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-7 (induced by source paper "good secretaries bad truck drivers occupational gender stereotypes in sentiment analysis"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "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"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"

Connected through hyperedge HE-8 (induced by source paper "gebiotoolkit automatic extraction of gender balanced multilingual corpus of wikipedia biographies"):
  with members:
    - "evaluating gender bias in machine translation"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "gender bias in coreference resolution"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "reducing gender bias in abusive language detection"
    - "margin based parallel corpus mining with multilingual sentence embeddings"

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)