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: "on tree based neural sentence modeling"
  Abstract: Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at this https URL

Connected through hyperedge HE-1 (induced by source paper "tree structured attention with hierarchical accumulation"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "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"
    - "self attention with relative position representations"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "unsupervised recurrent neural network grammars"):
  with members:
    - "unsupervised latent tree induction with deep inside outside recursive autoencoders"
    - "cooperative learning of disjoint syntax and semantics"
    - "finding syntax in human encephalography with beam search"
    - "unsupervised grammar induction with depth bounded pcfg"
    - "backpropagating through structured argmax using a spigot"
    - "inducing grammars with and for neural machine translation"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"

Connected through hyperedge HE-3 (induced by source paper "cooperative learning of disjoint syntax and semantics"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "latent tree learning with differentiable parsers shift reduce parsing and chart parsing"
    - "towards dynamic computation graphs via sparse latent structure"
    - "deep contextualized word representations"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "learning to generate reviews and discovering sentiment"
    - "automatically composing representation transformations as a means for generalization"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-4 (induced by source paper "visually grounded neural syntax acquisition"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "cooperative learning of disjoint syntax and semantics"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an imitation learning approach to unsupervised parsing"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "reinforcement learning from imperfect demonstrations"
    - "backpropagating through structured argmax using a spigot"
    - "hypothesis only baselines in natural language inference"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "grammar induction with neural language models an unusual replication"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "towards string to tree neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "cell aware stacked lstms for modeling sentences"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "deep contextualized word representations"
    - "enhancing sentence embedding with generalized pooling"
    - "learning to generate reviews and discovering sentiment"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "deep learning with dynamic computation graphs"
    - "fast slow recurrent neural networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "generative neural machine for tree structures"

Connected through hyperedge HE-8 (induced by source paper "learning to embed sentences using attentive recursive trees"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "structured attention networks"
    - "learned in translation contextualized word vectors"
    - "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
    - "do latent tree learning models identify meaningful structure in sentences"
    - "a structured self attentive sentence embedding"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "attentive pooling 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)