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: "comparing cnn and lstm character level embeddings in bilstm crf models for chemical and disease named entity recognition"
  Abstract: We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks. Empirical results over the BioCreative V CDR corpus show that the use of either type of character-level word embeddings in conjunction with the BiLSTM-CRF models leads to comparable state-of-the-art performance. However, the models using CNN-based character-level word embeddings have a computational performance advantage, increasing training time over word-based models by 25% while the LSTM-based character-level word embeddings more than double the required training time.

Connected through hyperedge HE-1 (induced by source paper "language independent tokenisation rivals language specific tokenisation for word similarity prediction"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "word similarity datasets for thai construction and evaluation"
    - "a systematic study of leveraging subword information for learning word representations"
    - "how multilingual is multilingual bert"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generalizing word embeddings using bag of subwords"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"

Connected through hyperedge HE-2 (induced by source paper "improving chemical named entity recognition in patents with contextualized word embeddings"):
  with members:
    - "using similarity measures to select pretraining data for ner"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

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)