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: "gated word character recurrent language model"
  Abstract: We introduce a recurrent neural network language model (RNN-LM) with long short-term memory (LSTM) units that utilizes both character-level and word-level inputs. Our model has a gate that adaptively finds the optimal mixture of the character-level and word-level inputs. The gate creates the final vector representation of a word by combining two distinct representations of the word. The character-level inputs are converted into vector representations of words using a bidirectional LSTM. The word-level inputs are projected into another high-dimensional space by a word lookup table. The final vector representations of words are used in the LSTM language model which predicts the next word given all the preceding words. Our model with the gating mechanism effectively utilizes the character-level inputs for rare and out-of-vocabulary words and outperforms word-level language models on several English corpora.

Connected through hyperedge HE-1 (induced by source paper "gating mechanisms for combining character and word level word representations an empirical study"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "learning universal sentence representations with mean max attention autoencoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-2 (induced by source paper "subword language model for query auto completion"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "personalized neural language models for real world query auto completion"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to discover ground and use words with segmental neural language models"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "personalized language model for query auto completion"
    - "distilling knowledge for search based structured prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-3 (induced by source paper "restoring ancient text using deep learning a case study on greek epigraphy"):
  with members:
    - "neural decipherment via minimum cost flow from ugaritic to linear b"
    - "effective character augmented word embedding for machine reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "the narrativeqa reading comprehension challenge"
    - "large scale cloze test dataset created by teachers"
    - "deep learning the indus script"
    - "embracing data abundance booktest dataset for reading comprehension"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (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"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "coherent dialogue with attention based language models"

Connected through hyperedge HE-5 (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"
    - "character level language modeling with hierarchical recurrent neural networks"
    - "generating chinese classical poems with rnn encoder decoder"
    - "neural paraphrase generation with stacked residual lstm networks"

Connected through hyperedge HE-6 (induced by source paper "effective subword segmentation for text comprehension"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "discourse marker augmented network with reinforcement learning for natural language inference"
    - "dual co matching network for multi choice reading comprehension"
    - "i know what you want semantic learning for text comprehension"
    - "graph based bilingual word embedding for statistical machine translation"
    - "open vocabulary learning for neural chinese pinyin ime"
    - "deep contextualized word representations"
    - "subword augmented embedding for cloze reading comprehension"

Connected through hyperedge HE-7 (induced by source paper "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"):
  with members:
    - "diagonal rnns in symbolic music modeling"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "dilated recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "comparative study of cnn and rnn for natural language processing"
    - "skip rnn learning to skip state updates in recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "trellis networks for sequence modeling"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"
    - "learning longer term dependencies in rnns with auxiliary losses"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "efficient neural architecture search via parameter sharing"

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)