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: "probabilistic top down parsing and language modeling"
  Abstract: This paper describes the functioning of a broad-coverage probabilistic top-down parser, and its application to the problem of language modeling for speech recognition. The paper first introduces key notions in language modeling and probabilistic parsing, and briefly reviews some previous approaches to using syntactic structure for language modeling. A lexicalized probabilistic top-down parser is then presented, which performs very well, in terms of both the accuracy of returned parses and the efficiency with which they are found, relative to the best broad-coverage statistical parsers. A new language model which utilizes probabilistic top-down parsing is then outlined, and empirical results show that it improves upon previous work in test corpus perplexity. Interpolation with a trigram model yields an exceptional improvement relative to the improvement observed by other models, demonstrating the degree to which the information captured by our parsing model is orthogonal to that captured by a trigram model. A small recognition experiment also demonstrates the utility of the model.

Connected through hyperedge HE-1 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "assessing bert s syntactic abilities"
    - "universal transformers"
    - "finding syntax in human encephalography with beam search"
    - "distilling knowledge for search based structured prediction"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "ordered memory"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "cooperative learning of disjoint syntax and semantics"
    - "listops a diagnostic dataset for latent tree learning"
    - "improved sentence modeling using suffix bidirectional lstm"
    - "systematic generalization what is required and can it be learned"
    - "deep contextualized word representations"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-3 (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-4 (induced by source paper "ordered neurons integrating tree structures into recurrent neural networks"):
  with members:
    - "the emergence of number and syntax units in lstm language models"
    - "on tree based neural sentence modeling"
    - "colorless green recurrent networks dream hierarchically"
    - "targeted syntactic evaluation of language models"
    - "grammar induction with neural language models an unusual replication"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-5 (induced by source paper "what can linguistics and deep learning contribute to each other"):
  with members:
    - "finding syntax in human encephalography with beam search"
    - "a neural model of adaptation in reading"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "assessing composition in sentence vector representations"
    - "on the practical computational power of finite precision rnns for language recognition"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "colorless green recurrent networks dream hierarchically"
    - "revisiting the poverty of the stimulus hierarchical generalization without a hierarchical bias in recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "incremental natural language processing challenges strategies and evaluation"):
  with members:
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "learning to translate in real time with neural machine translation"
    - "can neural machine translation do simultaneous translation"
    - "an incremental parser for abstract meaning representation"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "transition based dependency parsing with stack long short term memory"
    - "strongly incremental repair detection"

Connected through hyperedge HE-7 (induced by source paper "targeted syntactic evaluation of language models"):
  with members:
    - "assessing composition in sentence vector representations"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "neural network acceptability judgments"
    - "on the state of the art of evaluation in neural language models"
    - "frame semantic parsing with softmax margin segmental rnns and a syntactic scaffold"
    - "recurrent neural network grammars"

Connected through hyperedge HE-8 (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"

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)