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: "character n gram embeddings to improve rnn language models"
  Abstract: This paper proposes a novel Recurrent Neural Network (RNN) language model that takes advantage of character information. We focus on character n-grams based on research in the field of word embedding construction (Wieting et al. 2016). Our proposed method constructs word embeddings from character n-gram embeddings and combines them with ordinary word embeddings. We demonstrate that the proposed method achieves the best perplexities on the language modeling datasets: Penn Treebank, WikiText-2, and WikiText-103. Moreover, we conduct experiments on application tasks: machine translation and headline generation. The experimental results indicate that our proposed method also positively affects these tasks.

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"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "roberta a robustly optimized bert pretraining approach"
    - "deep contextualized word representations"

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"
    - "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"
    - "training of deep neural networks based on distance measures using rmsprop"

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)