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: "character level language modeling with hierarchical recurrent neural networks"
  Abstract: Recurrent neural network (RNN) based character-level language models (CLMs) are extremely useful for modeling out-of-vocabulary words by nature. However, their performance is generally much worse than the word-level language models (WLMs), since CLMs need to consider longer history of tokens to properly predict the next one. We address this problem by proposing hierarchical RNN architectures, which consist of multiple modules with different timescales. Despite the multi-timescale structures, the input and output layers operate with the character-level clock, which allows the existing RNN CLM training approaches to be directly applicable without any modifications. Our CLM models show better perplexity than Kneser-Ney (KN) 5-gram WLMs on the One Billion Word Benchmark with only 2% of parameters. Also, we present real-time character-level end-to-end speech recognition examples on the Wall Street Journal (WSJ) corpus, where replacing traditional mono-clock RNN CLMs with the proposed models results in better recognition accuracies even though the number of parameters are reduced to 30%.

Connected through hyperedge HE-1 (induced by source paper "improved hierarchical patient classification with language model pretraining over clinical notes"):
  with members:
    - "seqsleepnet end to end hierarchical recurrent neural network for sequence to sequence automatic sleep staging"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "scalable and accurate deep learning for electronic health records"

Connected through hyperedge HE-2 (induced by source paper "a survey on neural network language models"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "character word lstm language models"
    - "attention is all you need"
    - "a neural knowledge language model"
    - "theanolm an extensible toolkit for neural network language modeling"
    - "gated word character recurrent language model"
    - "coherent dialogue with attention based language models"

Connected through hyperedge HE-3 (induced by source paper "single headed attention rnn stop thinking with your head"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "fast transformer decoding one write head is all you need"
    - "an analysis of neural language modeling at multiple scales"
    - "character level language modeling with deeper self attention"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "neural poetry learning to generate poems using syllables"):
  with members:
    - "deep speare a joint neural model of poetic language meter and rhyme"
    - "an unsupervised character aware neural approach to word and context representation learning"
    - "adversarial generation of natural language"
    - "chinese song iambics generation with neural attention based model"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "gated word character recurrent language model"
    - "generating chinese classical poems with rnn encoder decoder"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-5 (induced by source paper "spell once summon anywhere a two level open vocabulary language model"):
  with members:
    - "deep contextualized word representations"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "mimicking word embeddings using subword rnns"
    - "regularizing and optimizing lstm language models"
    - "learning to create and reuse words in open vocabulary neural language modeling"
    - "morphological priors for probabilistic neural word embeddings"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-6 (induced by source paper "an unsupervised character aware neural approach to word and context representation learning"):
  with members:
    - "gated word character recurrent language model"
    - "exploring the limits of language modeling"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "bidirectional lstm crf models for sequence tagging"
    - "character aware neural language models"
    - "boosting named entity recognition with neural character embeddings"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "recurrent neural network language models for open vocabulary event level cyber anomaly detection"):
  with members:
    - "deep learning for unsupervised insider threat detection in structured cybersecurity data streams"
    - "character based neural machine translation"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "gated feedback recurrent neural networks"
    - "a clockwork rnn"
    - "scalable recommendation with poisson factorization"

Connected through hyperedge HE-8 (induced by source paper "input switched affine networks an rnn architecture designed for interpretability"):
  with members:
    - "lstm a search space odyssey"
    - "automatic rule extraction from long short term memory networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "end to end learning for self driving cars"
    - "exploring the limits of language modeling"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "visualizing and understanding recurrent networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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.LG(Machine Learning)