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: "learning structured text representations"
  Abstract: In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing rich structural dependencies. Specifically, we embed a differentiable non-projective parsing algorithm into a neural model and use attention mechanisms to incorporate the structural biases. Experimental evaluation across different tasks and datasets shows that the proposed model achieves state-of-the-art results on document modeling tasks while inducing intermediate structures which are both interpretable and meaningful.

Connected through hyperedge HE-1 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization"):
  with members:
    - "evaluating discourse in structured text representations"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "text summarization with pretrained encoders"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "bottom up abstractive summarization"
    - "toward abstractive summarization using semantic representations"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "structure infused copy mechanisms for abstractive summarization"

Connected through hyperedge HE-2 (induced by source paper "lp sparsemap differentiable relaxed optimization for sparse structured prediction"):
  with members:
    - "learning latent trees with stochastic perturbations and differentiable dynamic programming"
    - "sparse sequence to sequence models"
    - "differentiable convex optimization layers"
    - "meta learning with implicit gradients"
    - "listops a diagnostic dataset for latent tree learning"
    - "backpropagating through structured argmax using a spigot"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structured attention networks"

Connected through hyperedge HE-3 (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"
    - "on tree based neural sentence modeling"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "evaluation benchmarks and learning criteria for discourse aware sentence representations"):
  with members:
    - "what bert is not lessons from a new suite of psycholinguistic diagnostics for language models"
    - "enteval a holistic evaluation benchmark for entity representations"
    - "a unified linear time framework for sentence level discourse parsing"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "evaluating discourse in structured text representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "superglue a stickier benchmark for general purpose language understanding systems"

Connected through hyperedge HE-5 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "sanity checks for saliency maps"
    - "satirical news detection and analysis using attention mechanism and linguistic features"
    - "attention is all you need"
    - "neural discourse structure for text categorization"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-6 (induced by source paper "not all attention is needed gated attention network for sequence data"):
  with members:
    - "marginalized average attentional network for weakly supervised learning"
    - "star transformer"
    - "generating long sequences with sparse transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "structured attention networks"
    - "attention is all you need"
    - "convolutional networks with adaptive inference graphs"

Connected through hyperedge HE-7 (induced by source paper "hierarchical transformers for multi document summarization"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "structured neural summarization"
    - "newsroom a dataset of 1 3 million summaries with diverse extractive strategies"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generating wikipedia by summarizing long sequences"
    - "towards dynamic computation graphs via sparse latent structure"
    - "bottom up abstractive summarization"
    - "automatic detection of vague words and sentences in privacy policies"

Connected through hyperedge HE-8 (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"
    - "graph convolutional encoders for syntax aware neural machine translation"

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)