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: "the lifted matrix space model for semantic composition"
  Abstract: Recent advances in tree structured sentence encoding models have shown that explicitly modeling syntax can help handle compositionality. More specifically, recent works by \citetext{Socher2012}, \citetext{Socher2013}, and \citetext{Chen2013} have shown that using more powerful composition functions with multiplicative interactions within tree-structured models can yield significant improvements in model performance. However, existing compositional approaches which make use of these multiplicative interactions usually have to learn task-specific matrix-shaped word embeddings or rely on third-order tensors, which can be very costly. This paper introduces the Lifted Matrix-Space model which improves on the predecessors on this aspect. The model learns a global transformation from pre-trained word embeddings into matrices, which can be composed via matrix multiplication. The upshot is that we can capture the multiplicative interaction without learning matrix-valued word representations from scratch. In addition, our composition function effectively transmits a larger number of activations across layers with comparably few model parameters. We evaluate our model on the Stanford NLI corpus and the Multi-Genre NLI corpus and find that the Lifted Matrix-Space model outperforms the tree-structured long short-term memory networks.

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)