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: "compound probabilistic context free grammars for grammar induction"
  Abstract: We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar. In contrast to traditional formulations which learn a single stochastic grammar, our context-free rule probabilities are modulated by a per-sentence continuous latent variable, which induces marginal dependencies beyond the traditional context-free assumptions. Inference in this grammar is performed by collapsed variational inference, in which an amortized variational posterior is placed on the continuous variable, and the latent trees are marginalized with dynamic programming. Experiments on English and Chinese show the effectiveness of our approach compared to recent state-of-the-art methods for grammar induction.

Connected through hyperedge HE-1 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "inducing syntactic trees from bert representations"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "from balustrades to pierre vinken looking for syntax in transformer self attentions"
    - "assessing bert s syntactic abilities"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "a critical analysis of biased parsers in unsupervised parsing"

Connected through hyperedge HE-2 (induced by source paper "compositional neural machine translation by removing the lexicon from syntax"):
  with members:
    - "human few shot learning of compositional instructions"
    - "compositional generalization through meta sequence to sequence learning"
    - "good enough compositional data augmentation"
    - "compositional generalization in a deep seq2seq model by separating syntax and semantics"
    - "attention is all you need"
    - "data recombination for neural semantic parsing"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "tree transformer integrating tree structures into self attention"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "linguistic knowledge and transferability of contextual representations"
    - "visualizing attention in transformer based language representation models"
    - "unified language model pre training for natural language understanding and generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"

Connected through hyperedge HE-4 (induced by source paper "inducing constituency trees through neural machine translation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "an imitation learning approach to unsupervised parsing"
    - "inducing grammars with and for neural machine translation"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "linguistically informed self attention for semantic role labeling"
    - "grammar induction with neural language models an unusual replication"
    - "learning to parse and translate improves neural machine translation"
    - "towards neural machine translation with latent tree attention"

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)