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: "dependency grammar induction with neural lexicalization and big training data"
  Abstract: We study the impact of big models (in terms of the degree of lexicalization) and big data (in terms of the training corpus size) on dependency grammar induction. We experimented with L-DMV, a lexicalized version of Dependency Model with Valence and L-NDMV, our lexicalized extension of the Neural Dependency Model with Valence. We find that L-DMV only benefits from very small degrees of lexicalization and moderate sizes of training corpora. L-NDMV can benefit from big training data and lexicalization of greater degrees, especially when enhanced with good model initialization, and it achieves a result that is competitive with the current state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "visually grounded neural syntax acquisition"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "cooperative learning of disjoint syntax and semantics"
    - "on tree based neural sentence modeling"
    - "quantifying the visual concreteness of words and topics in multimodal datasets"
    - "visual coreference resolution in visual dialog using neural module networks"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "straight to the tree constituency parsing with neural syntactic distance"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of syntactic structure with invertible neural projections"):
  with members:
    - "deep contextualized word representations"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "i revnet deep invertible networks"
    - "crf autoencoder for unsupervised dependency parsing"
    - "density estimation using real nvp"
    - "language as a latent variable discrete generative models for sentence compression"
    - "unsupervised neural hidden markov models"
    - "unsupervised pos induction with word embeddings"

Connected through hyperedge HE-3 (induced by source paper "unsupervised grammar induction with depth bounded pcfg"):
  with members:
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"

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)