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: "first women second sex gender bias in wikipedia"
  Abstract: Contributing to the writing of history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia, and arguably one of the primary sources of knowledge on the Web. In this paper, we study gender bias in Wikipedia in terms of how women and men are characterized in their biographies. To do so, we analyze biographical content in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in social theory and discuss their implications for Wikipedia policy.

Connected through hyperedge HE-1 (induced by source paper "towards understanding gender bias in relation extraction"):
  with members:
    - "what s in a name reducing bias in bios without access to protected attributes"
    - "mitigating gender bias in natural language processing literature review"
    - "improving neural relation extraction with implicit mutual relations"
    - "opennre an open and extensible toolkit for neural relation extraction"
    - "neural relation extraction within and across sentence boundaries"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "it s all in the name mitigating gender bias with name based counterfactual data substitution"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "semantics derived automatically from language corpora contain human like biases"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-4 (induced by source paper "mind the gap a balanced corpus of gendered ambiguous pronouns"):
  with members:
    - "solving hard coreference problems"
    - "gender bias in coreference resolution"
    - "a simple method for commonsense reasoning"
    - "context aware neural machine translation learns anaphora resolution"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "end to end neural coreference resolution"
    - "attention is all you need"

Connected through hyperedge HE-5 (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"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"

Connected through hyperedge HE-6 (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"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "moodbar increasing new user retention in wikipedia through lightweight socialization"
    - "polyglot ner massive multilingual named entity recognition"

Connected through hyperedge HE-7 (induced by source paper "gender matters analyzing global cultural gender preferences for venues using social sensing"):
  with members:
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "the call of the crowd event participation in location based social services"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-8 (induced by source paper "women through the glass ceiling gender asymmetries in wikipedia"):
  with members:
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "interactions of cultures and top people of wikipedia from ranking of 24 language editions"
    - "biographical social networks on wikipedia a cross cultural study of links that made history"

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)