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: "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
  Abstract: Wikipedia is a community-created encyclopedia that contains information about notable people from different countries, epochs and disciplines and aims to document the world's knowledge from a neutral point of view. However, the narrow diversity of the Wikipedia editor community has the potential to introduce systemic biases such as gender biases into the content of Wikipedia. In this paper we aim to tackle a sub problem of this larger challenge by presenting and applying a computational method for assessing gender bias on Wikipedia along multiple dimensions. We find that while women on Wikipedia are covered and featured well in many Wikipedia language editions, the way women are portrayed starkly differs from the way men are portrayed. We hope our work contributes to increasing awareness about gender biases online, and in particular to raising attention to the different levels in which gender biases can manifest themselves on the web.

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"
    - "inferring gender from names on the web a comparative evaluation of gender detection methods"
    - "why should i trust you explaining the predictions of any classifier"
    - "character level convolutional networks for text classification"
    - "image based recommendations on styles and substitutes"

Connected through hyperedge HE-2 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models"):
  with members:
    - "right for the right reason training agnostic networks"
    - "explicit bias discovery in visual question answering models"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "learning gender neutral word embeddings"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "entity centric contextual affective analysis"):
  with members:
    - "assessing bert s syntactic abilities"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "measuring bias in contextualized word representations"
    - "contextual affective analysis a case study of people portrayals in online metoo stories"
    - "gender bias in contextualized word embeddings"
    - "publicly available clinical bert embeddings"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (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"
    - "women through the glass ceiling gender asymmetries in wikipedia"

Connected through hyperedge HE-7 (induced by source paper "neural based statement classification for biased language"):
  with members:
    - "selection bias in news coverage learning it fighting it"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "finding news citations for wikipedia"
    - "how much is wikipedia lagging behind news"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "measuring the importance of user generated content to search engines"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "botornot a system to evaluate social bots"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "not at home on the range peer production and the urban rural divide"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"
    - "query chains learning to rank from implicit feedback"

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)