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: "part of speech tagging with bidirectional long short term memory recurrent neural network"
  Abstract: Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten documents. While word embedding has been demoed as a powerful representation for characterizing the statistical properties of natural language. In this study, we propose to use BLSTM-RNN with word embedding for part-of-speech (POS) tagging task. When tested on Penn Treebank WSJ test set, a state-of-the-art performance of 97.40 tagging accuracy is achieved. Without using morphological features, this approach can also achieve a good performance comparable with the Stanford POS tagger.

Connected through hyperedge HE-1 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "causality extraction based on self attentive bilstm crf with transferred embeddings"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "measuring the influence of mere exposure effect of tv commercial adverts on purchase behavior based on machine learning prediction models"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "deep semantic role labeling with self attention"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-4 (induced by source paper "implicit dimension identification in user generated text with lstm networks"):
  with members:
    - "recurrent ladder networks"
    - "deep learning for hate speech detection in tweets"
    - "fake news detection on social media a data mining perspective"
    - "w2vlda almost unsupervised system for aspect based sentiment analysis"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "pointer sentinel mixture models"
    - "morphological inflection generation with hard monotonic attention"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-5 (induced by source paper "augmenting a bilstm tagger with a morphological lexicon and a lexical category identification step"):
  with members:
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "finding function in form compositional character models for open vocabulary word representation"

Connected through hyperedge HE-6 (induced by source paper "scheduled multi task learning from syntax to translation"):
  with members:
    - "learning to parse and translate improves neural machine translation"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "one model to learn them all"
    - "what do neural machine translation models learn about morphology"
    - "syntax aware neural machine translation using ccg"
    - "recurrent neural network grammars"

Connected through hyperedge HE-7 (induced by source paper "building context aware clause representations for situation entity type classification"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-8 (induced by source paper "simple models for word formation in english slang"):
  with members:
    - "charmanteau character embedding models for portmanteau creation"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "sequence to sequence neural net models for grapheme to phoneme conversion"
    - "adam a method for stochastic optimization"

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)