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: "understanding undesirable word embedding associations"
  Abstract: Word embeddings are often criticized for capturing undesirable word associations such as gender stereotypes. However, methods for measuring and removing such biases remain poorly understood. We show that for any embedding model that implicitly does matrix factorization, debiasing vectors post hoc using subspace projection (Bolukbasi et al., 2016) is, under certain conditions, equivalent to training on an unbiased corpus. We also prove that WEAT, the most common association test for word embeddings, systematically overestimates bias. Given that the subspace projection method is provably effective, we use it to derive a new measure of association called the $\textit{relational inner product association}$ (RIPA). Experiments with RIPA reveal that, on average, skipgram with negative sampling (SGNS) does not make most words any more gendered than they are in the training corpus. However, for gender-stereotyped words, SGNS actually amplifies the gender association in the corpus.

Connected through hyperedge HE-1 (induced by source paper "automatically neutralizing subjective bias in text"):
  with members:
    - "towards controlled transformation of sentiment in sentences"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating style transfer for text"
    - "identifying and reducing gender bias in word level language models"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "examining gender bias in languages with grammatical gender"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "attenuating bias in word vectors"
    - "evaluating the underlying gender bias in contextualized 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"
    - "identifying and reducing gender bias in word level language models"
    - "gender bias in coreference resolution"

Connected through hyperedge HE-3 (induced by source paper "rotate textit king to get textit queen word relationships as orthogonal transformations in embedding space"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "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"
    - "what does bert look at an analysis of bert s attention"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"

Connected through hyperedge HE-4 (induced by source paper "rotate king to get queen word relationships as orthogonal transformations in embedding space"):
  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"
    - "towards understanding linear word analogies"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "ultradense word embeddings by orthogonal transformation"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-5 (induced by source paper "probabilistic bias mitigation in word embeddings"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "advances in pre training distributed word representations"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "equality of opportunity in supervised learning"
    - "semantics derived automatically from language corpora necessarily contain human biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "distributed representations of words and phrases and their compositionality"

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)