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: "textual entailment with structured attentions and composition"
  Abstract: Deep learning techniques are increasingly popular in the textual entailment task, overcoming the fragility of traditional discrete models with hard alignments and logics. In particular, the recently proposed attention models (Rockt\"aschel et al., 2015; Wang and Jiang, 2015) achieves state-of-the-art accuracy by computing soft word alignments between the premise and hypothesis sentences. However, there remains a major limitation: this line of work completely ignores syntax and recursion, which is helpful in many traditional efforts. We show that it is beneficial to extend the attention model to tree nodes between premise and hypothesis. More importantly, this subtree-level attention reveals information about entailment relation. We study the recursive composition of this subtree-level entailment relation, which can be viewed as a soft version of the Natural Logic framework (MacCartney and Manning, 2009). Experiments show that our structured attention and entailment composition model can correctly identify and infer entailment relations from the bottom up, and bring significant improvements in accuracy.

Connected through hyperedge HE-1 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions"):
  with members:
    - "gated group self attention for answer selection"
    - "interpretable question answering on knowledge bases and text"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "on the validity of self attention as explanation in transformer models"
    - "is attention interpretable"
    - "probabilistic neural symbolic models for interpretable visual question answering"
    - "on variational bounds of mutual information"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-2 (induced by source paper "a bayesian approach to direct and inverse abstract argumentation problems"):
  with members:
    - "a large annotated corpus for learning natural language inference"
    - "value based argumentation frameworks"

Connected through hyperedge HE-3 (induced by source paper "same representation different attentions shareable sentence representation learning from multiple tasks"):
  with members:
    - "attention is all you need"
    - "adversarial multi task learning for text classification"
    - "an overview of multi task learning in deep neural networks"
    - "learning sentence representation with guidance of human attention"
    - "recurrent neural network for text classification with multi task learning"
    - "multi way multilingual neural machine translation with a shared attention mechanism"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "improving natural language inference using external knowledge in the science questions domain"):
  with members:
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "neural natural language inference models enhanced with external knowledge"
    - "comparative study of cnn and rnn for natural language processing"
    - "a broad coverage challenge corpus for sentence understanding through inference"

Connected through hyperedge HE-5 (induced by source paper "tag enhanced tree structured neural networks for implicit discourse relation classification"):
  with members:
    - "structural attention neural networks for improved sentiment analysis"
    - "adversarial connective exploiting networks for implicit discourse relation classification"
    - "tree to sequence attentional neural machine translation"
    - "enhanced lstm for natural language inference"
    - "a latent variable recurrent neural network for discourse relation language models"
    - "recognizing implicit discourse relations via repeated reading neural networks with multi level attention"
    - "improved semantic representations from tree structured long short term memory networks"
    - "when are tree structures necessary for deep learning of representations"

Connected through hyperedge HE-6 (induced by source paper "dr bilstm dependent reading bidirectional lstm for natural language inference"):
  with members:
    - "natural language inference over interaction space"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "neural semantic encoders"
    - "learning natural language inference with lstm"
    - "a large annotated corpus for learning natural language inference"
    - "ask me anything dynamic memory networks for natural language processing"

Connected through hyperedge HE-7 (induced by source paper "the diagrammatic ai language dial version 0 1"):
  with members:
    - "dlpaper2code auto generation of code from deep learning research papers"

Connected through hyperedge HE-8 (induced by source paper "structured attention networks"):
  with members:
    - "online segment to segment neural transduction"
    - "enhanced lstm for natural language inference"
    - "segmental recurrent neural networks for end to end speech recognition"
    - "neural architectures for named entity recognition"
    - "neural tree indexers for text understanding"
    - "the neural noisy channel"
    - "end to end memory networks"
    - "listen attend and spell"

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)