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: "dr bilstm dependent reading bidirectional lstm for natural language inference"
  Abstract: We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference. We also introduce a sophisticated ensemble strategy to combine our proposed models, which noticeably improves final predictions. Finally, we demonstrate how the results can be improved further with an additional preprocessing step. Our evaluation shows that DR-BiLSTM obtains the best single model and ensemble model results achieving the new state-of-the-art scores on the Stanford NLI dataset.

Connected through hyperedge HE-1 (induced by source paper "kernel graph attention network for fact verification"):
  with members:
    - "several experiments on investigating pretraining and knowledge enhanced models for natural language inference"
    - "bert for evidence retrieval and claim verification"
    - "reasoning over semantic level graph for fact checking"
    - "gear graph based evidence aggregating and reasoning for fact verification"
    - "understanding attention and generalization in graph neural networks"
    - "deeper text understanding for ir with contextual neural language modeling"
    - "cedr contextualized embeddings for document ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-2 (induced by source paper "multi perspective inferrer reasoning sentences relationship from holistic perspective"):
  with members:
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "dynamic past and future for neural machine translation"
    - "multi labeled relation extraction with attentive capsule network"
    - "hypothesis only baselines in natural language inference"
    - "information aggregation via dynamic routing for sequence encoding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "stochastic answer networks for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "heuristics for interpretable knowledge graph contextualization"):
  with members:
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "improving natural language inference using external knowledge in the science questions domain"
    - "neural natural language inference models enhanced with external knowledge"
    - "a decomposable attention model for natural language inference"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "learning natural language inference using bidirectional lstm model and inner attention"
    - "complex embeddings for simple link prediction"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-4 (induced by source paper "a question entailment approach to question answering"):
  with members:
    - "semeval 2016 task 3 community question answering"
    - "semeval 2017 task 3 community question answering"
    - "semantic sentence matching with densely connected recurrent and co attentive information"
    - "neural natural language inference models enhanced with external knowledge"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "cmu livemedqa at trec 2017 liveqa a consumer health question answering system"
    - "neural question answering at bioasq 5b"
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"

Connected through hyperedge HE-5 (induced by source paper "codah an adversarially authored question answer dataset for common sense"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "know what you don t know unanswerable questions for squad"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "attention is all you need"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "combining similarity features and deep representation learning for stance detection in the context of checking fake news"):
  with members:
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "universal transformers"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "automatic stance detection using end to end memory networks"
    - "co stack residual affinity networks with multi level attention refinement for matching text sequences"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "declare debunking fake news and false claims using evidence aware deep learning"
    - "debunking fake news one feature at a time"

Connected through hyperedge HE-7 (induced by source paper "co stack residual affinity networks with multi level attention refinement for matching text sequences"):
  with members:
    - "multi perspective relevance matching with hierarchical convnets for social media search"
    - "deep contextualized word representations"
    - "a continuously growing dataset of sentential paraphrases"
    - "natural language inference over interaction space"
    - "learned in translation contextualized word vectors"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "neural paraphrase identification of questions with noisy pretraining"

Connected through hyperedge HE-8 (induced by source paper "suffix bidirectional long short term memory"):
  with members:
    - "an efficient framework for learning sentence representations"
    - "ncrf an open source neural sequence labeling toolkit"
    - "learning to generate reviews and discovering sentiment"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "natural language inference over interaction space"
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"

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)