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: "expoiting syntactic structure for language modeling"
  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 and operates in a left-to-right manner --- therefore usable for automatic speech recognition. The model, its probabilistic parameterization, and a set of experiments meant to evaluate its predictive power are presented; an improvement over standard trigram modeling is achieved.

Connected through hyperedge HE-1 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "transformer xl attentive language models beyond a fixed length context"
    - "direct output connection for a high rank language model"
    - "backpropagating through structured argmax using a spigot"
    - "rational recurrences"
    - "syntactic scaffolds for semantic structures"
    - "learning joint semantic parsers from disjoint data"

Connected through hyperedge HE-2 (induced by source paper "good enough compositional data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "human few shot learning of compositional instructions"
    - "learning data manipulation for augmentation and weighting"
    - "improving text to sql evaluation methodology"
    - "deep contextualized word representations"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-3 (induced by source paper "the importance of generation order in language modeling"):
  with members:
    - "sharp nearby fuzzy far away how neural language models use context"
    - "attention is all you need"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "globally normalized transition based neural networks"
    - "log linear combinations of monolingual and bilingual neural machine translation models for automatic post editing"
    - "learning to compose words into sentences with reinforcement learning"
    - "exploring the limits of language modeling"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "probabilistic parsing strategies"):
  with members:
    - "efficient probabilistic top down and left corner parsing"

Connected through hyperedge HE-5 (induced by source paper "probabilistic top down parsing and language modeling"):
  with members:
    - "exploiting syntactic structure for natural language modeling"
    - "exploiting diversity in natural language processing combining parsers"
    - "recognition performance of a structured language model"
    - "estimators for stochastic unification based grammars"
    - "efficient probabilistic top down and left corner parsing"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"

Connected through hyperedge HE-6 (induced by source paper "robust probabilistic predictive syntactic processing"):
  with members:
    - "probabilistic top down parsing and language modeling"
    - "exploiting syntactic structure for natural language modeling"
    - "compact non left recursive grammars using the selective left corner transform and factoring"
    - "recognition performance of a structured language model"
    - "efficient probabilistic top down and left corner parsing"
    - "measuring efficiency in high accuracy broad coverage statistical parsing"
    - "refinement of a structured language model"

Connected through hyperedge HE-7 (induced by source paper "a bit of progress in language modeling"):
  with members:
    - "classes for fast maximum entropy training"

Connected through hyperedge HE-8 (induced by source paper "a classification approach to word prediction"):
  with members:
    - "a learning approach to shallow parsing"
    - "measures of distributional similarity"
    - "a winnow based approach to context sensitive spelling correction"
    - "learning to resolve natural language ambiguities a unified approach"
    - "similarity based models of word cooccurrence probabilities"

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)