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: "unsupervised recurrent neural network grammars"
  Abstract: Recurrent neural network grammars (RNNG) are generative models of language which jointly model syntax and surface structure by incrementally generating a syntax tree and sentence in a top-down, left-to-right order. Supervised RNNGs achieve strong language modeling and parsing performance, but require an annotated corpus of parse trees. In this work, we experiment with unsupervised learning of RNNGs. Since directly marginalizing over the space of latent trees is intractable, we instead apply amortized variational inference. To maximize the evidence lower bound, we develop an inference network parameterized as a neural CRF constituency parser. On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese. On constituency grammar induction, they are competitive with recent neural language models that induce tree structures from words through attention mechanisms.

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:
    - "inducing syntactic trees from bert representations"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "compound probabilistic context free grammars for grammar induction"
    - "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 "analysis of diversity accuracy tradeoff in image captioning"):
  with members:
    - "the curious case of neural text degeneration"
    - "mixture models for diverse machine translation tricks of the trade"
    - "describing like humans on diversity in image captioning"
    - "learning to write with cooperative discriminators"
    - "hierarchical neural story generation"
    - "adversarial inference for multi sentence video description"
    - "attention is all you need"
    - "speaking the same language matching machine to human captions by adversarial training"

Connected through hyperedge HE-3 (induced by source paper "tree structured attention with hierarchical accumulation"):
  with members:
    - "tree transformer a transformer based method for correction of tree structured data"
    - "pay less attention with lightweight and dynamic convolutions"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "on tree based neural sentence modeling"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism"):
  with members:
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "measuring compositionality in representation learning"
    - "analyzing the structure of attention in a transformer language model"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "the emergence of number and syntax units in lstm language models"
    - "open sesame getting inside bert s linguistic knowledge"

Connected through hyperedge HE-5 (induced by source paper "scalable syntax aware language models using knowledge distillation"):
  with members:
    - "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"
    - "the importance of being recurrent for modeling hierarchical structure"

Connected through hyperedge HE-6 (induced by source paper "compound probabilistic context free grammars for grammar induction"):
  with members:
    - "scalable syntax aware language models using knowledge distillation"
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "an imitation learning approach to unsupervised parsing"
    - "text generation with exemplar based adaptive decoding"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "visually grounded neural syntax acquisition"
    - "structural supervision improves learning of non local grammatical dependencies"

Connected through hyperedge HE-7 (induced by source paper "palm a hybrid parser and language model"):
  with members:
    - "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"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "visually grounded neural syntax acquisition"):
  with members:
    - "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"
    - "ordered neurons integrating tree structures into recurrent neural networks"

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)