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: "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
  Abstract: Gender stereotypes are manifest in most of the world's languages and are consequently propagated or amplified by NLP systems. Although research has focused on mitigating gender stereotypes in English, the approaches that are commonly employed produce ungrammatical sentences in morphologically rich languages. We present a novel approach for converting between masculine-inflected and feminine-inflected sentences in such languages. For Spanish and Hebrew, our approach achieves F1 scores of 82% and 73% at the level of tags and accuracies of 90% and 87% at the level of forms. By evaluating our approach using four different languages, we show that, on average, it reduces gender stereotyping by a factor of 2.5 without any sacrifice to grammaticality.

Connected through hyperedge HE-1 (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"
    - "the geometry of culture analyzing the meanings of class through word embeddings"

Connected through hyperedge HE-2 (induced by source paper "learning the difference that makes a difference with counterfactually augmented data"):
  with members:
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what do deep networks like to read"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "breaking nli systems with sentences that require simple lexical inferences"

Connected through hyperedge HE-3 (induced by source paper "counterfactual vision and language navigation via adversarial path sampling"):
  with members:
    - "adversarial active exploration for inverse dynamics model learning"
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "stay on the path instruction fidelity in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "transferable representation learning in vision and language navigation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "counterfactual visual explanations"
    - "learning to navigate unseen environments back translation with environmental dropout"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "how does grammatical gender affect noun representations in gender marking languages"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "demorphy german language morphological analyzer"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "learning gender neutral word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "the interplay of semantics and morphology in word embeddings"
    - "man is to computer programmer as woman is to homemaker debiasing 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)