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: "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
  Abstract: We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification. MGNC-CNN extracts features from input embedding sets independently and then joins these at the penultimate layer in the network to form a final feature vector. We then adopt a group regularization strategy that differentially penalizes weights associated with the subcomponents generated from the respective embedding sets. This model is much simpler than comparable alternative architectures and requires substantially less training time. Furthermore, it is flexible in that it does not require input word embeddings to be of the same dimensionality. We show that MGNC-CNN consistently outperforms baseline models.

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 "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"
    - "learning text representation using recurrent convolutional neural network with highway layers"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for argumentation mining"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "lstm a search space odyssey"
    - "neural end to end learning for computational argumentation mining"
    - "multi task learning for mental health using social media text"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "attention strategies for multi source sequence to sequence learning"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "cross lingual and cross domain discourse segmentation of entire documents"

Connected through hyperedge HE-4 (induced by source paper "topic sensitive attention on generic corpora corrects sense bias in pretrained embeddings"):
  with members:
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning general purpose distributed sentence representations via large scale multi task learning"

Connected through hyperedge HE-5 (induced by source paper "fusing vector space models for domain specific applications"):
  with members:
    - "lifelong domain word embedding via meta learning"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "deep contextualized word representations"
    - "phrase based neural unsupervised machine translation"
    - "comparative study of cnn and rnn for natural language processing"
    - "a simple regularization based algorithm for learning cross domain word embeddings"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "multichannel variable size convolution for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "self balanced dropout"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "multichannel variable size convolution for sentence classification"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "on the inductive bias of dropout"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "from random to supervised a novel dropout mechanism integrated with global information"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "a structured self attentive sentence embedding"
    - "harnessing deep neural networks with logic rules"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "skip thought vectors"
    - "improved semantic representations from tree structured long short term memory networks"
    - "distributed representations of sentences and documents"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "sentence modeling via multiple word embeddings and multi level comparison for semantic textual similarity"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "learning generic sentence representations using convolutional neural networks"
    - "enriching word vectors with subword information"
    - "charagram embedding words and sentences via character n grams"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

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)