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: "getting gender right in neural machine translation"
  Abstract: Speakers of different languages must attend#R##N#to and encode strikingly different aspects of#R##N#the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such#R##N#difference is related to the way gender is expressed in a language. Saying “I am happy”#R##N#in English, does not encode any additional#R##N#knowledge of the speaker that uttered the sentence. However, many other languages do#R##N#have grammatical gender systems and so such#R##N#knowledge would be encoded. In order to#R##N#correctly translate such a sentence into, say,#R##N#French, the inherent gender information needs#R##N#to be retained/recovered. The same sentence#R##N#would become either “Je suis heureux”, for a#R##N#male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.)#R##N#also influence our use of language in terms of#R##N#word choices or even on the level of syntactic constructions (Tannen, 1991; Pennebaker#R##N#et al., 2003). We integrate gender information#R##N#into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with#R##N#speaker information for 20 language pairs, and#R##N#(2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that#R##N#adding a gender feature to an NMT system significantly improves the translation quality for#R##N#some language pairs.

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"
    - "assessing gender bias in machine translation a case study with google translate"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "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"
    - "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"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "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"
    - "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 "equalizing gender biases in neural machine translation with word embeddings techniques"):
  with members:
    - "assessing gender bias in machine translation a case study with google translate"
    - "judging a book by its description analyzing gender stereotypes in the man bookers prize winning fiction"
    - "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-6 (induced by source paper "machine translation for machines the sentiment classification use case"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "can neural machine translation be improved with user feedback"
    - "a study of reinforcement learning for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

Connected through hyperedge HE-7 (induced by source paper "controlling text complexity in neural machine translation"):
  with members:
    - "multi task neural models for translating between styles within and across languages"
    - "sockeye a toolkit for neural machine translation"
    - "sentence simplification with deep reinforcement learning"
    - "personalized machine translation preserving original author traits"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "lost in translation loss and decay of linguistic richness in machine translation"):
  with members:
    - "assessing gender bias in machine translation a case study with google translate"
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "neural versus phrase based machine translation quality a case study"
    - "improving neural machine translation models with monolingual data"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)