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: "do convolutional networks need to be deep for text classification"
  Abstract: We study in this work the importance of depth in convolutional models for text classification, either when character or word inputs are considered. We show on 5 standard text classification and sentiment analysis tasks that deep models indeed give better performances than shallow networks when the text input is represented as a sequence of characters. However, a simple shallow-and-wide network outperforms deep models such as DenseNet with word inputs. Our shallow word model further establishes new state-of-the-art performances on two datasets: Yelp Binary (95.9\%) and Yelp Full (64.9\%).

Connected through hyperedge HE-1 (induced by source paper "word class embeddings for multiclass text classification"):
  with members:
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "enhancing domain word embedding via latent semantic imputation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep learning for sentiment analysis a survey"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-2 (induced by source paper "to tune or not to tune how about the best of both worlds"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "how to fine tune bert for text classification"
    - "bert and pals projected attention layers for efficient adaptation in multi task learning"
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "linguistic knowledge and transferability of contextual representations"
    - "deep visual domain adaptation a survey"

Connected through hyperedge HE-3 (induced by source paper "sector a neural model for coherent topic segmentation and classification"):
  with members:
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "characterizing question facets for complex answer retrieval"
    - "structuring wikipedia articles with section recommendations"
    - "text segmentation as a supervised learning task"
    - "benchmark for complex answer retrieval"
    - "getting deep recommenders fit bloom embeddings for sparse binary input output networks"
    - "wikireading a novel large scale language understanding task over wikipedia"
    - "very deep convolutional networks for text classification"

Connected through hyperedge HE-4 (induced by source paper "squeezed very deep convolutional neural networks for text classification"):
  with members:
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "depthwise separable convolutions for neural machine translation"
    - "very deep convolutional networks for text classification"
    - "densely connected convolutional networks"
    - "xception deep learning with depthwise separable convolutions"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-5 (induced by source paper "legal area classification a comparative study of text classifiers on singapore supreme court judgments"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "predicting the law area and decisions of french supreme court cases"
    - "replace or retrieve keywords in documents at scale"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "argument identification in public comments from erulemaking"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "parsing argumentation structures in persuasive essays"
    - "bag of tricks for efficient text classification"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "using deep learning for title based semantic subject indexing to reach competitive performance to full text"):
  with members:
    - "lstm a search space odyssey"
    - "on the state of the art of evaluation in neural language models"
    - "amplifying the impact of open access wikipedia and the diffusion of science"
    - "comparative study of cnn and rnn for natural language processing"
    - "recent trends in deep learning based natural language processing"
    - "deep extreme multi label learning"
    - "using titles vs full text as source for automated semantic document annotation"
    - "multichannel variable size convolution for sentence classification"

Connected through hyperedge HE-8 (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"
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "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"

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)