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: "semantics derived automatically from language corpora necessarily contain human biases"
  Abstract: Artificial intelligence and machine learning are in a period of astounding growth. However, there are concerns that these technologies may be used, either with or without intention, to perpetuate the prejudice and unfairness that unfortunately characterizes many human institutions. Here we show for the first time that human-like semantic biases result from the application of standard machine learning to ordinary language---the same sort of language humans are exposed to every day. We replicate a spectrum of standard human biases as exposed by the Implicit Association Test and other well-known psychological studies. We replicate these using a widely used, purely statistical machine-learning model---namely, the GloVe word embedding---trained on a corpus of text from the Web. Our results indicate that language itself contains recoverable and accurate imprints of our historic biases, whether these are morally neutral as towards insects or flowers, problematic as towards race or gender, or even simply veridical, reflecting the status quo for the distribution of gender with respect to careers or first names. These regularities are captured by machine learning along with the rest of semantics. In addition to our empirical findings concerning language, we also contribute new methods for evaluating bias in text, the Word Embedding Association Test (WEAT) and the Word Embedding Factual Association Test (WEFAT). Our results have implications not only for AI and machine learning, but also for the fields of psychology, sociology, and human ethics, since they raise the possibility that mere exposure to everyday language can account for the biases we replicate here.

Connected through hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "explainable reinforcement learning through a causal lens"
    - "learning interpretable models with causal guarantees"
    - "explaining classifiers with causal concept effect cace"
    - "neural network attributions a causal perspective"
    - "on the accuracy of influence functions for measuring group effects"
    - "explaining deep learning models with constrained adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "getting fairness right towards a toolbox for practitioners"):
  with members:
    - "identifying and correcting label bias in machine learning"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "discrimination in online ad delivery"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "gmail smart compose real time assisted writing"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "cross lingual language model pretraining"
    - "personalized language model for query auto completion"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "lightweight adaptive mixture of neural and n gram language models"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (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"
    - "getting gender right in neural machine translation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "evaluating the underlying gender bias in contextualized word embeddings"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "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"
    - "linguistic knowledge and transferability of contextual representations"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "parallax visualizing and understanding the semantics of embedding spaces via algebraic formulae"):
  with members:
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "starspace embed all the things"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "item2vec neural item embedding for collaborative filtering"
    - "node2vec scalable feature learning for networks"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "wordrank learning word embeddings via robust ranking"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"

Connected through hyperedge HE-7 (induced by source paper "probabilistic bias mitigation in word embeddings"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "understanding undesirable word embedding associations"
    - "advances in pre training distributed word representations"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "open the black box data driven explanation of black box decision systems"):
  with members:
    - "local rule based explanations of black box decision systems"
    - "a survey of methods for explaining black box models"
    - "fairness testing testing software for discrimination"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "exposing the probabilistic causal structure of discrimination"
    - "causal discrimination discovery through propensity score analysis"
    - "why should i trust you explaining the predictions of any classifier"
    - "show attend and tell neural image caption generation with visual attention"

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.AI(Artificial Intelligence)