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: "assessing gender bias in machine translation a case study with google translate"
  Abstract: Recently there has been a growing concern about machine bias, where trained statistical models grow to reflect controversial societal asymmetries, such as gender or racial bias. A significant number of AI tools have recently been suggested to be harmfully biased towards some minority, with reports of racist criminal behavior predictors, Iphone X failing to differentiate between two Asian people and Google photos' mistakenly classifying black people as gorillas. Although a systematic study of such biases can be difficult, we believe that automated translation tools can be exploited through gender neutral languages to yield a window into the phenomenon of gender bias in AI. #R##N#In this paper, we start with a comprehensive list of job positions from the U.S. Bureau of Labor Statistics (BLS) and used it to build sentences in constructions like "He/She is an Engineer" in 12 different gender neutral languages such as Hungarian, Chinese, Yoruba, and several others. We translate these sentences into English using the Google Translate API, and collect statistics about the frequency of female, male and gender-neutral pronouns in the translated output. We show that GT exhibits a strong tendency towards male defaults, in particular for fields linked to unbalanced gender distribution such as STEM jobs. We ran these statistics against BLS' data for the frequency of female participation in each job position, showing that GT fails to reproduce a real-world distribution of female workers. We provide experimental evidence that even if one does not expect in principle a 50:50 pronominal gender distribution, GT yields male defaults much more frequently than what would be expected from demographic data alone. #R##N#We are hopeful that this work will ignite a debate about the need to augment current statistical translation tools with debiasing techniques which can already be found in the scientific literature.

Connected through hyperedge HE-1 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

Connected through hyperedge HE-2 (induced by source paper "mitigating gender bias in natural language processing literature review"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "model cards for model reporting"
    - "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"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "women also snowboard overcoming bias in captioning models"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-3 (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-4 (induced by source paper "does gender matter towards fairness in dialogue systems"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "a survey on bias and fairness in machine learning"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "identifying and reducing gender bias in word level language models"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "gender bias in neural natural language processing"

Connected through hyperedge HE-5 (induced by source paper "toward gender inclusive coreference resolution"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "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"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "gender bias in coreference resolution"
    - "automatic reference based evaluation of pronoun translation misses the point"

Connected through hyperedge HE-6 (induced by source paper "gender preserving debiasing for pre trained word embeddings"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "gender bias in coreference resolution"
    - "deep contextualized word representations"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adversarial removal of demographic attributes from text data"
    - "learning gender neutral word embeddings"
    - "gender bias in coreference resolution evaluation and debiasing methods"

Connected through hyperedge HE-7 (induced by source paper "equalizing gender biases in neural machine translation with word embeddings techniques"):
  with members:
    - "judging a book by its description analyzing gender stereotypes in the man bookers prize winning fiction"
    - "getting gender right in neural machine translation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "reducing gender bias in abusive language detection"
    - "learning gender neutral word embeddings"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "attention is all you need"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"

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"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "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.CY(Computers and Society)