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: "recurrent neural network based sentence encoder with gated attention for natural language inference"
  Abstract: The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task. This paper describes our system (alpha) that is ranked among the top in the Shared Task, on both the in-domain test set (obtaining a 74.9% accuracy) and on the cross-domain test set (also attaining a 74.9% accuracy), demonstrating that the model generalizes well to the cross-domain data. Our model is equipped with intra-sentence gated-attention composition which helps achieve a better performance. In addition to submitting our model to the Shared Task, we have also tested it on the Stanford Natural Language Inference (SNLI) dataset. We obtain an accuracy of 85.5%, which is the best reported result on SNLI when cross-sentence attention is not allowed, the same condition enforced in RepEval 2017.

Connected through hyperedge HE-1 (induced by source paper "sequential neural networks for noetic end to end response selection"):
  with members:
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "sequential attention based network for noetic end to end response selection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhancing sentence embedding with generalized pooling"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "making neural machine reading comprehension faster"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "a fully attention based information retriever"
    - "lightweight convolutional approaches to reading comprehension on squad"
    - "phase conductor on multi layered attentions for machine comprehension"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "learning to compute word embeddings on the fly"
    - "contextualized word representations for reading comprehension"

Connected through hyperedge HE-3 (induced by source paper "discourse marker augmented network with reinforcement learning for natural language inference"):
  with members:
    - "natural language inference over interaction space"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a compare propagate architecture with alignment factorization for natural language inference"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"

Connected through hyperedge HE-4 (induced by source paper "sentence embeddings in nli with iterative refinement encoders"):
  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"
    - "enhancing sentence embedding with generalized pooling"
    - "dynamic self attention computing attention over words dynamically for sentence embedding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "testing the generalization power of neural network models across nli benchmarks"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "equate a benchmark evaluation framework for quantitative reasoning in natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "what knowledge is needed to solve the rte5 textual entailment challenge"
    - "hypothesis only baselines in natural language inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stress test evaluation for natural language inference"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "sequential attention based network for noetic end to end response selection"):
  with members:
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhancing sentence embedding with generalized pooling"
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "neural natural language inference models enhanced with external knowledge"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "a structured self attentive sentence embedding"
    - "enhanced lstm for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "cell aware stacked lstms for modeling sentences"):
  with members:
    - "on tree based neural sentence modeling"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "tensorized self attention efficiently modeling pairwise and global dependencies together"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "generating wikipedia by summarizing long sequences"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "enhancing sentence embedding with generalized pooling"
    - "natural language inference with hierarchical bilstm max pooling architecture"
    - "linguistically informed self attention for semantic role labeling"

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)