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: "jointly learning sentence embeddings and syntax with unsupervised tree lstms"
  Abstract: We introduce a neural network that represents sentences by composing their words according to induced binary parse trees. We use Tree-LSTM as our composition function, applied along a tree structure found by a fully differentiable natural language chart parser. Our model simultaneously optimises both the composition function and the parser, thus eliminating the need for externally-provided parse trees which are normally required for Tree-LSTM. It can therefore be seen as a tree-based RNN that is unsupervised with respect to the parse trees. As it is fully differentiable, our model is easily trained with an off-the-shelf gradient descent method and backpropagation. We demonstrate that it achieves better performance compared to various supervised Tree-LSTM architectures on a textual entailment task and a reverse dictionary task.

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"
    - "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"

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 "learning to compose and reason with language tree structures for visual grounding"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "rethinking diversified and discriminative proposal generation for visual grounding"
    - "inferring and executing programs for visual reasoning"
    - "grounding referring expressions in images by variational context"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "comprehension guided referring expressions"
    - "learning to reason end to end module networks for visual question answering"
    - "learning to segment every thing"

Connected through hyperedge HE-4 (induced by source paper "cooperative learning of disjoint syntax and semantics"):
  with members:
    - "on tree based neural sentence modeling"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "learning latent trees with stochastic perturbations and differentiable dynamic programming"):
  with members:
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "towards dynamic computation graphs via sparse latent structure"
    - "a tree based decoder for neural machine translation"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "grammar induction with neural language models an unusual replication"
    - "structured attention networks"
    - "learning structured text representations"

Connected through hyperedge HE-6 (induced by source paper "unsupervised latent tree induction with deep inside outside recursive autoencoders"):
  with members:
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "ordered neurons integrating tree structures into recurrent neural networks"
    - "dissecting contextual word embeddings architecture and representation"
    - "grammar induction with neural language models an unusual replication"
    - "learning to parse and translate improves neural machine translation"
    - "incorporating syntactic uncertainty in neural machine translation with forest to sequence model"

Connected through hyperedge HE-7 (induced by source paper "an imitation learning approach to unsupervised parsing"):
  with members:
    - "unsupervised recurrent neural network grammars"
    - "reinforcement learning from imperfect demonstrations"
    - "on tree based neural sentence modeling"
    - "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"
    - "towards string to tree neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "modeling latent sentence structure in neural machine translation"):
  with members:
    - "inducing grammars with and for neural machine translation"
    - "structured attention networks"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "tree to sequence attentional neural machine translation"
    - "learning to compose words into sentences with reinforcement learning"

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)