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: "women through the glass ceiling gender asymmetries in wikipedia"
  Abstract: Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world’s knowledge from a neutral point of view. Though everyone can participate it is well known that the editor community has a narrow diversity, with a majority of white male editors. While this participatory gender gap has been studied extensively in the literature, this work sets out to assess potential gender inequalities in Wikipedia articles along different dimensions: notability, topical focus, linguistic bias, structural properties, and meta-data presentation. We find that (i) women in Wikipedia are more notable than men, which we interpret as the outcome of a subtle glass ceiling effect; (ii) family-, gender-, and relationship-related topics are more present in biographies about women; (iii) linguistic bias manifests in Wikipedia since abstract terms tend to be used to describe positive aspects in the biographies of men and negative aspects in the biographies of women; and (iv) there are structural differences in terms of meta-data and hyperlinks, which have consequences for information-seeking activities. While some differences are expected, due to historical and social contexts, other differences are attributable to Wikipedia editors. The implications of such differences are discussed having Wikipedia contribution policies in mind. We hope that the present work will contribute to increased awareness about, first, gender issues in the content of Wikipedia, and second, the different levels on which gender biases can manifest on the Web.

Connected through hyperedge HE-1 (induced by source paper "knowledge graphs"):
  with members:
    - "modl a modular ontology design library"
    - "drum end to end differentiable rule mining on knowledge graphs"
    - "hypernetwork knowledge graph embeddings"
    - "ceres distantly supervised relation extraction from the semi structured web"
    - "a tutorial on modular ontology modeling with ontology design patterns the cooking recipes ontology"
    - "building an ecosystem for the tyrolean tourism knowledge graph"
    - "eventkg a multilingual event centric temporal knowledge graph"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "measuring social bias in knowledge graph embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluating gender bias in machine translation"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "ernie enhanced language representation with informative entities"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "compositional fairness constraints for graph embeddings"

Connected through hyperedge HE-3 (induced by source paper "automatically inferring gender associations from language"):
  with members:
    - "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"
    - "analyzing polarization in social media method and application to tweets on 21 mass shootings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "learning gender neutral word embeddings"
    - "semantics derived automatically from language corpora contain human like biases"
    - "tie breaker using language models to quantify gender bias in sports journalism"
    - "shirtless and dangerous quantifying linguistic signals of gender bias in an online fiction writing community"

Connected through hyperedge HE-4 (induced by source paper "gebiotoolkit automatic extraction of gender balanced multilingual corpus of wikipedia biographies"):
  with members:
    - "evaluating gender bias in machine translation"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "wikimatrix mining 135m parallel sentences in 1620 language pairs from wikipedia"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "gender bias in coreference resolution"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "reducing gender bias in abusive language detection"

Connected through hyperedge HE-5 (induced by source paper "analyzing gender inequality through large scale facebook advertising data"):
  with members:
    - "professional gender gaps across us cities"
    - "facebook use of sensitive data for advertising in europe"
    - "studying migrant assimilation through facebook interests"
    - "using facebook ads audiences for global lifestyle disease surveillance promises and limitations"
    - "fertility and its meaning evidence from search behavior"
    - "evolution of privacy loss in wikipedia"
    - "gender asymmetries in reality and fiction the bechdel test of social media"

Connected through hyperedge HE-6 (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"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-7 (induced by source paper "online petitioning through data exploration and what we found there a dataset of petitions from avaaz org"):
  with members:
    - "facebook s gender divide"
    - "the rise of social bots"
    - "petition growth and success rates on the uk no 10 downing street website"

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"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "from migration corridors to clusters the value of google data for migration studies"
    - "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)