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: "on measuring social biases in sentence encoders"
  Abstract: The Word Embedding Association Test shows that GloVe and word2vec word embeddings exhibit human-like implicit biases based on gender, race, and other social constructs (Caliskan et al., 2017). Meanwhile, research on learning reusable text representations has begun to explore sentence-level texts, with some sentence encoders seeing enthusiastic adoption. Accordingly, we extend the Word Embedding Association Test to measure bias in sentence encoders. We then test several sentence encoders, including state-of-the-art methods such as ELMo and BERT, for the social biases studied in prior work and two important biases that are difficult or impossible to test at the word level. We observe mixed results including suspicious patterns of sensitivity that suggest the test's assumptions may not hold in general. We conclude by proposing directions for future work on measuring bias in sentence encoders.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "compositional fairness constraints for graph embeddings"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "scalable fair clustering"

Connected through hyperedge HE-2 (induced by source paper "measuring social biases in grounded vision and language embeddings"):
  with members:
    - "assessing social and intersectional biases in contextualized word representations"
    - "large scale pretraining for visual dialog a simple state of the art baseline"
    - "iterative answer prediction with pointer augmented multimodal transformers for textvqa"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "visualbert a simple and performant baseline for vision and language"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"

Connected through hyperedge HE-3 (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"
    - "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"
    - "stable and fair classification"

Connected through hyperedge HE-4 (induced by source paper "mitigating gender bias in natural language processing literature review"):
  with members:
    - "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"
    - "examining gender and race bias in two hundred sentiment analysis systems"

Connected through hyperedge HE-5 (induced by source paper "pretrained ai models performativity mobility and change"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "vilbert pretraining task agnostic visiolinguistic representations for vision and language tasks"
    - "the ethics of ai ethics an evaluation of guidelines"
    - "end to end open domain question answering with"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "discrimination in the age of algorithms"

Connected through hyperedge HE-6 (induced by source paper "assessing social and intersectional biases in contextualized word representations"):
  with members:
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "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"
    - "understanding the origins of bias in word embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "sentence bert sentence embeddings using siamese bert networks"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "bertscore evaluating text generation with bert"
    - "understanding the behaviors of bert in ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic textual similarity from conversations"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"

Connected through hyperedge HE-8 (induced by source paper "does gender matter towards fairness in dialogue systems"):
  with members:
    - "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"
    - "gender bias in neural natural language processing"

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)