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: "inferring gender from names on the web a comparative evaluation of gender detection methods"
  Abstract: Computational social scientists often harness the Web as a "societal observatory" where data about human social behavior is collected. This data enables novel investigations of psychological, anthropological and sociological research questions. However, in the absence of demographic information, such as gender, many relevant research questions cannot be addressed. To tackle this problem, researchers often rely on automated methods to infer gender from name information provided on the web. However, little is known about the accuracy of existing gender-detection methods and how biased they are against certain sub-populations. In this paper, we address this question by systematically comparing several gender detection methods on a random sample of scientists for whom we know their full name, their gender and the country of their workplace.#R##N##R##N#We further suggest a novel method that employs web-based image retrieval and gender recognition in facial images in order to augment name-based approaches. Our findings show that the performance of name-based gender detection approaches can be biased towards countries of origin and such biases can be reduced by combining name-based an image-based gender detection methods.

Connected through hyperedge HE-1 (induced by source paper "the effects of gender signals and performance in online product reviews"):
  with members:
    - "gender differences in participation and reward on stack overflow"
    - "gendered behavior as a disadvantage in open source software development"
    - "why do men get more attention exploring factors behind success in an online design community"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "why should i trust you explaining the predictions of any classifier"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "character level convolutional networks for text classification"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-2 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "sampling from social networks with attributes"

Connected through hyperedge HE-3 (induced by source paper "gender differences in participation and reward on stack overflow"):
  with members:
    - "why do men get more attention exploring factors behind success in an online design community"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-4 (induced by source paper "through a gender lens learning usage patterns of emojis from large scale android users"):
  with members:
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "emoji2vec learning emoji representations from their description"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "line large scale information network embedding"
    - "sentiment of emojis"
    - "privacy tradeoffs in predictive analytics"

Connected through hyperedge HE-5 (induced by source paper "gendered behavior as a disadvantage in open source software development"):
  with members:
    - "a study of whatsapp usage patterns and prediction models without message content"
    - "why do men get more attention exploring factors behind success in an online design community"

Connected through hyperedge HE-6 (induced by source paper "white man and highly followed gender and race inequalities in twitter"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "demographics of news sharing in the u s twittersphere"
    - "linguistic diversities of demographic groups in twitter"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "from migration corridors to clusters the value of google data for migration studies"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "gendered conversation in a social game streaming platform"

Connected through hyperedge HE-7 (induced by source paper "demographics of news sharing in the u s twittersphere"):
  with members:
    - "what gets media attention and how media attention evolves over time large scale empirical evidence from 196 countries"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "greysanatomy vs yankees demographics and hashtag use on twitter"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "breaking the news first impressions matter on online news"
    - "when is it biased assessing the representativeness of twitter s streaming api"
    - "learning deep face representation"
    - "influence and passivity in social media"

Connected through hyperedge HE-8 (induced by source paper "weitergeleitet von journalistin the gendered presentation of professions on wikipedia"):
  with members:
    - "tie breaker using language models to quantify gender bias in sports journalism"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "moodbar increasing new user retention in wikipedia through lightweight socialization"
    - "first women second sex gender bias in wikipedia"
    - "polyglot ner massive multilingual named entity recognition"

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.CY(Computers and Society)