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: "semi supervised multi task word embeddings"
  Abstract: Word embeddings have been shown to benefit from ensembling several word embedding sources, often carried out using straightforward mathematical operations over the set of vectors to produce a meta-embedding representation. More recently, unsupervised learning has been used to find a lower-dimensional representation, similar in size to that of the word embeddings within the ensemble. However, these methods do not use the available manual labeled datasets that are often used solely for the purpose of evaluation. #R##N#We propose to improve word embeddings by simultaneously learning to reconstruct an ensemble of pretrained word embeddings with supervision from various labeled word similarity datasets. This involves reconstructing word meta-embeddings while simultaneously using a Siamese Network to also learn word similarity where both processes share a hidden layer. Experiments are carried out on 6 word similarity datasets and 3 analogy datasets. We find that performance is improved for all word similarity datasets when compared to unsupervised learning methods with a mean increase of 11.33 in the Spearman Correlation coefficient. Moreover, 4 of 6 of word similarity datasets from our approach show best performance when using of a cosine loss for reconstruction and Brier's loss for word similarity.

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)