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: "gender bias in neural natural language processing"
  Abstract: We examine whether neural natural language processing (NLP) systems reflect historical biases in training data. We define a general benchmark to quantify gender bias in a variety of neural NLP tasks. Our empirical evaluation with state-of-the-art neural coreference resolution and textbook RNN-based language models trained on benchmark datasets finds significant gender bias in how models view occupations. We then mitigate bias with CDA: a generic methodology for corpus augmentation via causal interventions that breaks associations between gendered and gender-neutral words. We empirically show that CDA effectively decreases gender bias while preserving accuracy. We also explore the space of mitigation strategies with CDA, a prior approach to word embedding debiasing (WED), and their compositions. We show that CDA outperforms WED, drastically so when word embeddings are trained. For pre-trained embeddings, the two methods can be effectively composed. We also find that as training proceeds on the original data set with gradient descent the gender bias grows as the loss reduces, indicating that the optimization encourages bias; CDA mitigates this behavior.

Connected through hyperedge HE-1 (induced by source paper "superglue a stickier benchmark for general purpose language understanding systems"):
  with members:
    - "boolq exploring the surprising difficulty of natural yes no questions"
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "paws paraphrase adversaries from word scrambling"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "a surprisingly robust trick for the winograd schema challenge"
    - "multi task deep neural networks for natural language understanding"
    - "improving multi task deep neural networks via knowledge distillation for natural language understanding"
    - "human vs muppet a conservative estimate of human performance on the glue benchmark"

Connected through hyperedge HE-2 (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"
    - "evaluating the underlying gender bias in contextualized word embeddings"

Connected through hyperedge HE-3 (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-4 (induced by source paper "undersensitivity in neural reading comprehension"):
  with members:
    - "learning to ask unanswerable questions for machine reading comprehension"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "multiqa an empirical investigation of generalization and transfer in reading comprehension"
    - "roberta a robustly optimized bert pretraining approach"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "what makes reading comprehension questions easier"

Connected through hyperedge HE-5 (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"
    - "assessing gender bias in machine translation a case study with google translate"

Connected through hyperedge HE-6 (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"
    - "counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "towards understanding gender bias in relation extraction"):
  with members:
    - "what s in a name reducing bias in bios without access to protected attributes"
    - "mitigating gender bias in natural language processing literature review"
    - "improving neural relation extraction with implicit mutual relations"
    - "opennre an open and extensible toolkit for neural relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-8 (induced by source paper "conceptor debiasing of word representations evaluated on weat"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "continual learning for sentence representations using conceptors"
    - "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"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "correcting the common discourse bias in linear representation of sentences using conceptors"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"

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)