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: "learning text representation using recurrent convolutional neural network with highway layers"
  Abstract: Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the input. The experiment shows that our model outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment analysis task. Besides, the analysis of how sequence length influences the RCNN with highway layers shows that our model could learn good representation for the long text.

Connected through hyperedge HE-1 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "a simple joint model for improved contextual neural lemmatization"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "word embeddings a survey"
    - "a robust hybrid approach for textual document classification"
    - "character n gram embeddings to improve rnn language models"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "a precisely xtreme multi channel hybrid approach for roman urdu sentiment analysis"):
  with members:
    - "from web crawled text to project descriptions automatic summarizing of social innovation projects"
    - "an analysis of hierarchical text classification using word embeddings"
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "character n gram embeddings to improve rnn language models"
    - "word class embeddings for multiclass text classification"
    - "using text analysis to quantify the similarity and evolution of scientific disciplines"
    - "sentiment classification of customer s reviews about automobiles in roman urdu"
    - "dynamic word embeddings for evolving semantic discovery"

Connected through hyperedge HE-3 (induced by source paper "hybrid tiled convolutional neural networks for text sentiment classification"):
  with members:
    - "parameterized convolutional neural networks for aspect level sentiment classification"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "constructing a word similarity graph from vector based word representation for named entity recognition"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "comparative study of cnn and rnn for natural language processing"
    - "exploring different dimensions of attention for uncertainty detection"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial nets for information retrieval fundamentals and advances"):
  with members:
    - "neural text generation past present and beyond"
    - "cot cooperative training for generative modeling of discrete data"
    - "texygen a benchmarking platform for text generation models"
    - "kbgan adversarial learning for knowledge graph embeddings"
    - "neural collaborative filtering"
    - "adversarial ranking for language generation"
    - "maximum likelihood augmented discrete generative adversarial networks"
    - "graphgan graph representation learning with generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "party matters enhancing legislative embeddings with author attributes for vote prediction"):
  with members:
    - "language modeling with gated convolutional networks"
    - "convolutional neural networks for sentence classification"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-6 (induced by source paper "comparative study of cnn and rnn for natural language processing"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "language modeling with gated convolutional networks"
    - "enhancing freebase question answering using textual evidence"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "a large annotated corpus for learning natural language inference"
    - "traversing knowledge graphs in vector space"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "text classification improved by integrating bidirectional lstm with two dimensional max pooling"):
  with members:
    - "multichannel variable size convolution for sentence classification"
    - "long short term memory networks for machine reading"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "recurrent neural network for text classification with multi task learning"
    - "self adaptive hierarchical sentence model"
    - "compositional distributional semantics with long short term memory"
    - "improved semantic representations from tree structured long short term memory networks"
    - "effective lstms for target dependent sentiment 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)