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: "a structured language model"
  Abstract: The paper presents a language model that develops syntactic structure and uses it to extract meaningful information from the word history, thus enabling the use of long distance dependencies. The model assigns probability to every joint sequence of words - binary-parse-structure with headword annotation. The model, its probabilistic parametrization, and a set of experiments meant to evaluate its predictive power are presented.

Connected through hyperedge HE-1 (induced by source paper "generating sentences from disentangled syntactic and semantic spaces"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "bag of words as target for neural machine translation"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "syntax directed variational autoencoder for structured data"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"

Connected through hyperedge HE-2 (induced by source paper "neural language modeling by jointly learning syntax and lexicon"):
  with members:
    - "on the state of the art of evaluation in neural language models"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "tree structured neural machine for linguistics aware sentence generation"
    - "long short term memory networks for machine reading"
    - "recurrent neural network grammars"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent highway networks"
    - "a fast unified model for parsing and sentence understanding"

Connected through hyperedge HE-3 (induced by source paper "exploiting syntactic structure for natural language modeling"):
  with members:
    - "expoiting syntactic structure for language modeling"

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)