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: "an unbiased approach to quantification of gender inclination using interpretable word representations"
  Abstract: Recent advances in word embedding provide significant benefit to various information processing tasks. Yet these dense representations and their estimation of word-to-word relatedness remain difficult to interpret and hard to analyze. As an alternative, explicit word representations i.e. vectors with clearly-defined dimensions, which can be words, windows of words, or documents are easily interpretable, and recent methods show competitive performance to the dense vectors. In this work, we propose a method to transfer word2vec SkipGram embedding model to its explicit representation model. The method provides interpretable explicit vectors while keeping the effectiveness of the original model, tested by evaluating the model on several word association collections. Based on the proposed explicit representation, we propose a novel method to quantify the degree of the existence of gender bias in the English language (used in Wikipedia) with regard to a set of occupations. By measuring the bias towards explicit Female and Male factors, the work demonstrates a general tendency of the majority of the occupations to male and a strong bias in a few specific occupations (e.g. nurse) to female.

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)