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: "neural tree indexers for text understanding"
  Abstract: Neural networks with recurrent or recursive architecture have shown promising results on various natural language processing (NLP) tasks. The recurrent and recursive architectures have their own strength and limitations. The recurrent networks process input text sequentially and model the conditional transition between word tokens. In contrast, the recursive networks explicitly model the compositionality and the recursive structure of natural language. Current recursive architecture is based on syntactic tree, thus limiting its practical applicability in different NLP applications. In this paper, we introduce a class of tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and different forms of node function. We demonstrated the effectiveness and the flexibility of a binary-tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "understanding roles and entities datasets and models for natural language inference"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "multi task deep neural networks for natural language understanding"
    - "annotation artifacts in natural language inference data"
    - "transforming question answering datasets into natural language inference datasets"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "large scale qa srl parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-4 (induced by source paper "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "hypothesis only baselines in natural language inference"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what if we simply swap the two text fragments a straightforward yet effective way to test the robustness of methods to confounding signals in nature language inference tasks"

Connected through hyperedge HE-5 (induced by source paper "incorporating domain knowledge into medical nli using knowledge graphs"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "probing biomedical embeddings from language models"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural natural language inference models enhanced with external knowledge"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "knowledge base completion baselines strike back"

Connected through hyperedge HE-6 (induced by source paper "unity in diversity learning distributed heterogeneous sentence representation for extractive summarization"):
  with members:
    - "hybrid memnet for extractive summarization"
    - "neural summarization by extracting sentences and words"
    - "improved semantic representations from tree structured long short term memory networks"
    - "tree structured composition in neural networks without tree structured architectures"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "recurrently controlled recurrent networks"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "deep contextualized word representations"
    - "multi range reasoning for machine comprehension"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "learning to generate reviews and discovering sentiment"
    - "sentence state lstm for text representation"

Connected through hyperedge HE-8 (induced by source paper "deep contextualized word representations"):
  with members:
    - "end to end neural coreference resolution"
    - "natural language inference over interaction space"
    - "on the state of the art of evaluation in neural language models"
    - "learned in translation contextualized word vectors"
    - "stochastic answer networks for machine reading comprehension"
    - "semi supervised sequence tagging with bidirectional language models"
    - "regularizing and optimizing lstm language models"
    - "what do neural machine translation models learn about morphology"

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)