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: "deep learning applied to nlp"
  Abstract: Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP.

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 "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "multi resolution networks for semantic segmentation in whole slide images"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "attention is all you need"
    - "gp unet lesion detection from weak labels with a 3d regression network"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "ca refinenet a dual input wsi image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "convolutional sequence to sequence learning"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "language agnostic syllabification with neural sequence labeling"):
  with members:
    - "syllabification by phone categorization"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "bidirectional lstm crf models for sequence tagging"

Connected through hyperedge HE-5 (induced by source paper "self supervised contextual language representation of radiology reports to improve the identification of communication urgency"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "publicly available clinical bert embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "multi task self supervised visual learning"
    - "word2vec parameter learning explained"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "classifying multilingual user feedback using traditional machine learning and deep learning"):
  with members:
    - "who is addressed in this comment automatically classifying meta comments in news comments"
    - "fasttext zip compressing text classification models"
    - "recent trends in deep learning based natural language processing"
    - "easy over hard a case study on deep learning"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "a study of recent contributions on information extraction"):
  with members:
    - "a survey of deep learning methods for relation extraction"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "relation extraction from clinical texts using domain invariant convolutional neural network"
    - "combining neural networks and log linear models to improve relation extraction"

Connected through hyperedge HE-8 (induced by source paper "putting self supervised token embedding on the tables"):
  with members:
    - "neural architectures for named entity recognition"
    - "a hierarchical neural autoencoder for paragraphs and documents"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "character aware neural language models"
    - "named entity recognition with bidirectional lstm cnns"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

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)