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: "improving generalization of vocal tract feature reconstruction from augmented acoustic inversion to articulatory feature reconstruction without articulatory data"
  Abstract: We address the problem of reconstructing articulatory movements, given audio and/or phonetic labels. The scarce availability of multi-speaker articulatory data makes it difficult to learn a reconstruction that generalizes to new speakers and across datasets. We first consider the XRMB dataset where audio, articulatory measurements and phonetic transcriptions are available. We show that phonetic labels, used as input to deep recurrent neural networks that reconstruct articulatory features, are in general more helpful than acoustic features in both matched and mismatched training-testing conditions. In a second experiment, we test a novel approach that attempts to build articulatory features from prior articulatory information extracted from phonetic labels. Such approach recovers vocal tract movements directly from an acoustic-only dataset without using any articulatory measurement. Results show that articulatory features generated by this approach can correlate up to 0.59 Pearson product-moment correlation with measured articulatory features.

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)