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: "natural language inference with hierarchical bilstm max pooling architecture"
  Abstract: Recurrent neural networks have proven to be very effective for natural language inference tasks. We build on top of one such model, namely BiLSTM with max pooling, and show that adding a hierarchy of BiLSTM and max pooling layers yields state of the art results for the SNLI sentence encoding-based models and the SciTail dataset, as well as provides strong results for the MultiNLI dataset. We also show that our sentence embeddings can be utilized in a wide variety of transfer learning tasks, outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks. Furthermore, our model beats the InferSent model in 8 out of 10 recently published SentEval probing tasks designed to evaluate sentence embeddings' ability to capture some of the important linguistic properties of sentences.

Connected through hyperedge HE-1 (induced by source paper "textnas a neural architecture search space tailored for text representation"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "deep contextualized word representations"
    - "darts differentiable architecture search"
    - "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"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-2 (induced by source paper "eliciting knowledge from experts automatic transcript parsing for cognitive task analysis"):
  with members:
    - "deep contextualized word representations"
    - "learning cognitive models using neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generative adversarial network for abstractive text summarization"
    - "empower sequence labeling with task aware neural language model"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "sequence to sequence rnns for text summarization"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-3 (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"
    - "linguistically informed self attention for semantic role labeling"
    - "simple recurrent units for highly parallelizable recurrence"

Connected through hyperedge HE-4 (induced by source paper "attention boosted sequential inference model"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "neural network models for paraphrase identification semantic textual similarity natural language inference and question answering"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "bilateral multi perspective matching for natural language sentences"
    - "distance based self attention network for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "testing the generalization power of neural network models across nli benchmarks"):
  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"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "performance impact caused by hidden bias of training data for recognizing textual entailment"

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)