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: "equalizing gender biases in neural machine translation with word embeddings techniques"
  Abstract: Neural machine translation has significantly pushed forward the quality of the field. However, there are remaining big issues with the translations and one of them is fairness. Neural models are trained on large text corpora which contains biases and stereotypes. As a consequence, models inherit these social biases. Recent methods have shown results in reducing gender bias in other natural language processing applications such as word embeddings. We take advantage of the fact that word embeddings are used in neural machine translation to propose the first debiased machine translation system. Specifically, we propose, experiment and analyze the integration of two debiasing techniques over GloVe embeddings in the Transformer translation architecture. We evaluate our proposed system on a generic English-Spanish task, showing gains up to one BLEU point. As for the gender bias evaluation, we generate a test set of occupations and we show that our proposed system learns to equalize existing biases from the baseline system.

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"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"
    - "stable and fair classification"

Connected through hyperedge HE-2 (induced by source paper "analysis of explainers of black box deep neural networks for computer vision a survey"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "attacking optical flow"
    - "a general framework for adversarial examples with objectives"
    - "on the in fidelity and sensitivity for explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visualbackprop efficient visualization of cnns"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"
    - "the memad submission to the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "gender bias in contextualized word embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "solving hard coreference problems"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "gender bias in coreference resolution"
    - "getting gender right in neural machine translation"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "toward gender inclusive coreference resolution"):
  with members:
    - "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"
    - "assessing gender bias in machine translation a case study with google translate"
    - "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 "examining gender bias in languages with grammatical gender"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "attenuating bias in word vectors"
    - "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"
    - "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 "evaluating gender bias in machine translation"):
  with members:
    - "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"
    - "demorphy german language morphological analyzer"
    - "gender bias in coreference resolution"
    - "scaling neural machine translation"
    - "adversarial removal of demographic attributes from text data"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "man is to person as woman is to location measuring gender bias in named entity recognition"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "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"
    - "gender bias in contextualized word embeddings"
    - "identifying and reducing gender bias in word level language models"
    - "understanding the origins of bias in word 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)