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: "who makes trends understanding demographic biases in crowdsourced recommendations"
  Abstract: Users of social media sites like Facebook and Twitter rely on crowdsourced content recommendation systems (e.g., Trending Topics) to retrieve important and useful information. Contents selected for recommendation indirectly give the initial users who promoted (by liking or posting) the content an opportunity to propagate their messages to a wider audience. Hence, it is important to understand the demographics of people who make a content worthy of recommendation, and explore whether they are representative of the media site's overall population. In this work, using extensive data collected from Twitter, we make the first attempt to quantify and explore the demographic biases in the crowdsourced recommendations. Our analysis, focusing on the selection of trending topics, finds that a large fraction of trends are promoted by crowds whose demographics are significantly different from the overall Twitter population. More worryingly, we find that certain demographic groups are systematically under-represented among the promoters of the trending topics. To make the demographic biases in Twitter trends more transparent, we developed and deployed a Web-based service 'Who-Makes-Trends' at this http URL.

Connected through hyperedge HE-1 (induced by source paper "fairface face attribute dataset for balanced race gender and age"):
  with members:
    - "diversity in faces"
    - "understanding the political ideology of legislators from social media images"
    - "persuasive faces generating faces in advertisements"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "mitigating unwanted biases with adversarial learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"

Connected through hyperedge HE-3 (induced by source paper "what is fair exploring pareto efficiency for fairness constraint classifiers"):
  with members:
    - "delayed impact of fair machine learning"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "the price of fair pca one extra dimension"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-4 (induced by source paper "public sphere 2 0 targeted commenting in online news media"):
  with members:
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "inside the right leaning echo chambers characterizing gab an unmoderated social system"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "white man and highly followed gender and race inequalities in twitter"
    - "fake news detection on social media a data mining perspective"
    - "analyzing the targets of hate in online social media"
    - "breaking the news first impressions matter on online news"

Connected through hyperedge HE-6 (induced by source paper "equality of voice towards fair representation in crowdsourced top k recommendations"):
  with members:
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "beyond parity fairness objectives for collaborative filtering"
    - "multisided fairness for recommendation"
    - "fa ir a fair top k ranking algorithm"
    - "axiomatic characterization of committee scoring rules"
    - "properties of multiwinner voting rules"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-7 (induced by source paper "image as data automated visual content analysis for political science"):
  with members:
    - "protest activity detection and perceived violence estimation from social media images"
    - "generating visual explanations"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "vqa visual question answering"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-8 (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"
    - "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"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "gendered conversation in a social game streaming platform"

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.SI(Social and Information Networks)