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: "convolutional neural networks for text categorization shallow word level vs deep character level"
  Abstract: This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing of Conneau et al. The shallow word-level CNN uses more parameters and therefore requires more storage than the deep character-level CNN; however, the shallow word-level CNN computes much faster.

Connected through hyperedge HE-1 (induced by source paper "a unified system for aggression identification in english code mixed and uni lingual texts"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "universal language model fine tuning for text classification"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"

Connected through hyperedge HE-2 (induced by source paper "aggressionnet generalised multi modal deep temporal and sequential learning for aggression identification"):
  with members:
    - "hierarchical taxonomy aware and attentional graph capsule rcnns for large scale multi label text classification"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "towards generating long and coherent text with multi level latent variable models"
    - "mattnet modular attention network for referring expression comprehension"
    - "lstms with attention for aggression detection"
    - "ritual uh at trac 2018 shared task aggression identification"
    - "aggression annotated corpus of hindi english code mixed data"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-3 (induced by source paper "facilitating sql query composition and analysis"):
  with members:
    - "query2vec nlp meets databases for generalized workload analytics"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "a survey of machine learning for big code and naturalness"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "very deep convolutional networks for text classification"
    - "database meets deep learning challenges and opportunities"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "finetext text classification via attention based language model fine tuning"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "selective encoding for abstractive sentence summarization"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-5 (induced by source paper "empirical comparisons of cnn with other learning algorithms for text classification in legal document review"):
  with members:
    - "empirical study of deep learning for text classification in legal document review"
    - "very deep convolutional networks for natural language processing"
    - "very deep convolutional networks for text classification"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "a primer on neural network models for natural language processing"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "exploiting effective representations for chinese sentiment analysis using a multi channel convolutional neural network"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "very deep convolutional networks for text classification"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "effective use of word order for text categorization with convolutional neural networks"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-7 (induced by source paper "hash embeddings for efficient word representations"):
  with members:
    - "very deep convolutional networks for natural language processing"
    - "neural machine translation with characters and hierarchical encoding"
    - "hash2vec feature hashing for word embeddings"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "entropy based pruning of backoff language models"

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)