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: "tie breaker using language models to quantify gender bias in sports journalism"
  Abstract: Gender bias is an increasingly important issue in sports journalism. In this work, we propose a language-model-based approach to quantify differences in questions posed to female vs. male athletes, and apply it to tennis post-match interviews. We find that journalists ask male players questions that are generally more focused on the game when compared with the questions they ask their female counterparts. We also provide a fine-grained analysis of the extent to which the salience of this bias depends on various factors, such as question type, game outcome or player rank.

Connected through hyperedge HE-1 (induced by source paper "mitigating gender bias in natural language processing literature review"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "model cards for model reporting"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "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"
    - "mind the gap a balanced corpus of gendered ambiguous pronouns"
    - "assessing gender bias in machine translation a case study with google translate"
    - "women also snowboard overcoming bias in captioning models"

Connected through hyperedge HE-2 (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-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"
    - "women through the glass ceiling gender asymmetries in wikipedia"
    - "shirtless and dangerous quantifying linguistic signals of gender bias in an online fiction writing community"

Connected through hyperedge HE-4 (induced by source paper "investigating sports commentator bias within a large corpus of american football broadcasts"):
  with members:
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "deep contextualized word representations"
    - "semantics derived automatically from language corpora contain human like biases"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-5 (induced by source paper "weitergeleitet von journalistin the gendered presentation of professions on wikipedia"):
  with members:
    - "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"
    - "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.CL(Computation and Language)