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: "lexicon integrated cnn models with attention for sentiment analysis"
  Abstract: With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and provides a global view of the document using attention. Our models are experimented on both the SemEval'16 Task 4 dataset and the Stanford Sentiment Treebank, and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings allow to build high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.

Connected through hyperedge HE-1 (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"
    - "learning text representation using recurrent convolutional neural network with highway layers"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "robust lexical features for improved neural network named entity recognition"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-3 (induced by source paper "using structured representation and data a hybrid model for negation and sentiment in customer service conversations"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "a combined cnn and lstm model for arabic sentiment analysis"):
  with members:
    - "improving sentiment analysis in arabic using word representation"
    - "gradascent at emoint 2017 character and word level recurrent neural network models for tweet emotion intensity detection"
    - "combining lexical features and a supervised learning approach for arabic sentiment analysis"
    - "arabic language sentiment analysis on health services"
    - "multichannel variable size convolution for sentence classification"
    - "enriching word vectors with subword information"
    - "feature representation in convolutional neural networks"
    - "sentiment analysis for modern standard arabic and colloquial"

Connected through hyperedge HE-5 (induced by source paper "attention incorporate network a network can adapt various data size"):
  with members:
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "deep networks with internal selective attention through feedback connections"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fractional max pooling"
    - "stochastic pooling for regularization of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "phramacovigilance exploring deep learning techniques for identifying mentions of medication intake from twitter"):
  with members:
    - "densely connected bidirectional lstm with applications to sentence classification"
    - "do convolutional networks need to be deep for text classification"
    - "text classification improved by integrating bidirectional lstm with two dimensional max pooling"
    - "recurrent neural network for text classification with multi task learning"
    - "improved semantic representations from tree structured long short term memory networks"
    - "going deeper with convolutions"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "post processing of word representations via variance normalization and dynamic embedding"):
  with members:
    - "evaluating word embedding models methods and experimental results"
    - "deep contextualized word representations"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "all but the top simple and effective postprocessing for word representations"
    - "problems with evaluation of word embeddings using word similarity tasks"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "did you take the pill detecting personal intake of medicine from twitter"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "infynlp at smm4h task 2 stacked ensemble of shallow convolutional neural networks for identifying personal medication intake from twitter"
    - "stronger baselines for trustable results in neural machine translation"
    - "do convolutional networks need to be deep for text classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence 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)