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: "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
  Abstract: Recurrent Neural Network (RNN) is one of the most popular architectures used in Natural Language Processsing (NLP) tasks because its recurrent structure is very suitable to process variable-length text. RNN can utilize distributed representations of words by first converting the tokens comprising each text into vectors, which form a matrix. And this matrix includes two dimensions: the time-step dimension and the feature vector dimension. Then most existing models usually utilize one-dimensional (1D) max pooling operation or attention-based operation only on the time-step dimension to obtain a fixed-length vector. However, the features on the feature vector dimension are not mutually independent, and simply applying 1D pooling operation over the time-step dimension independently may destroy the structure of the feature representation. On the other hand, applying two-dimensional (2D) pooling operation over the two dimensions may sample more meaningful features for sequence modeling tasks. To integrate the features on both dimensions of the matrix, this paper explores applying 2D max pooling operation to obtain a fixed-length representation of the text. This paper also utilizes 2D convolution to sample more meaningful information of the matrix. Experiments are conducted on six text classification tasks, including sentiment analysis, question classification, subjectivity classification and newsgroup classification. Compared with the state-of-the-art models, the proposed models achieve excellent performance on 4 out of 6 tasks. Specifically, one of the proposed models achieves highest accuracy on Stanford Sentiment Treebank binary classification and fine-grained classification tasks.

Connected through hyperedge HE-1 (induced by source paper "sentence level human translation quality estimation with attention based neural networks"):
  with members:
    - "unsupervised cross lingual representation learning at scale"
    - "pairwise neural machine translation evaluation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "machine translation evaluation with neural networks"
    - "xgboost a scalable tree boosting system"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "improving the interpretability of neural sentiment classifiers via data augmentation"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "attention is not explanation"
    - "generating token level explanations for natural language inference"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "transformation networks for target oriented sentiment classification"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "training augmentation with adversarial examples for robust speech recognition"

Connected through hyperedge HE-3 (induced by source paper "distilling task specific knowledge from bert into simple neural networks"):
  with members:
    - "assessing bert s syntactic abilities"
    - "training and inference with integers in deep neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "flops as a direct optimization objective for learning sparse neural networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "empirical analysis of multi task learning for reducing model bias in toxic comment detection"):
  with members:
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "machine learning suites for online toxicity detection"
    - "challenges for toxic comment classification an in depth error analysis"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "sarcasm analysis using conversation context"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"

Connected through hyperedge HE-5 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model"):
  with members:
    - "reference less quality estimation of text simplification systems"
    - "learning when not to answer a ternary reward structure for reinforcement learning based question answering"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "april interactively learning to summarise by combining active preference learning and reinforcement learning"
    - "towards diverse text generation with inverse reinforcement learning"
    - "a machine learning approach to persian text readability assessment using a crowdsourced dataset"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-6 (induced by source paper "pre train interact fine tune a novel interaction representation for text classification"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "exploiting document knowledge for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learned in translation contextualized word vectors"
    - "regularizing and optimizing lstm language models"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-7 (induced by source paper "label aware document representation via hybrid attention for extreme multi label text classification"):
  with members:
    - "attentionxml extreme multi label text classification with multi label attention based recurrent neural networks"
    - "sgm sequence generation model for multi label classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "explicit interaction model towards text classification"
    - "deep extreme multi label learning"
    - "a structured self attentive sentence embedding"
    - "node2vec scalable feature learning for networks"
    - "neural semantic encoders"

Connected through hyperedge HE-8 (induced by source paper "progress notes classification and keyword extraction using attention based deep learning models with bert"):
  with members:
    - "publicly available clinical bert embeddings"
    - "deep learning for sentiment analysis a survey"
    - "cross type biomedical named entity recognition with deep multi task learning"
    - "fine tuned language models for text classification"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "very deep convolutional networks for text classification"

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)