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: "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
  Abstract: Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014). However, these models require practitioners to specify an exact model architecture and set accompanying hyperparameters, including the filter region size, regularization parameters, and so on. It is currently unknown how sensitive model performance is to changes in these configurations for the task of sentence classification. We thus conduct a sensitivity analysis of one-layer CNNs to explore the effect of architecture components on model performance; our aim is to distinguish between important and comparatively inconsequential design decisions for sentence classification. We focus on one-layer CNNs (to the exclusion of more complex models) due to their comparative simplicity and strong empirical performance, which makes it a modern standard baseline method akin to Support Vector Machine (SVMs) and logistic regression. We derive practical advice from our extensive empirical results for those interested in getting the most out of CNNs for sentence classification in real world settings.

Connected through hyperedge HE-1 (induced by source paper "text classification with lexicon from preattention mechanism"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "no training required exploring random encoders for sentence classification"
    - "lifelong learning for sentiment classification"
    - "topic memory networks for short text classification"
    - "ncrf an open source neural sequence labeling toolkit"
    - "deriving machine attention from human rationales"
    - "a structured self attentive sentence embedding"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

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 "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "detection of the prodromal phase of bipolar disorder from psychological and phonological aspects in social media"
    - "advances in pre training distributed word representations"
    - "enriching word vectors with subword information"
    - "identifying depression on twitter"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks for sentiment analysis in persian social media"):
  with members:
    - "enriching word vectors with subword information"
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "persian sentiment analyzer a framework based on a novel feature selection method"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "unsupervised statistical machine translation"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-6 (induced by source paper "explainability in human agent systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "methods for interpreting and understanding deep neural networks"
    - "textual explanations for self driving vehicles"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explainable neural computation via stack neural module networks"
    - "providing explanations for recommendations in reciprocal environments"
    - "explainable software analytics"

Connected through hyperedge HE-7 (induced by source paper "automatically learning construction injury precursors from text"):
  with members:
    - "energy based self attentive learning of abstractive communities for spoken language understanding"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a structured self attentive sentence embedding"
    - "enriching word vectors with subword information"
    - "word embeddings for the construction domain"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-8 (induced by source paper "show your work improved reporting of experimental results"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "model cards for model reporting"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "sentence encoders on stilts supplementary training on intermediate labeled data tasks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "do cifar 10 classifiers generalize to cifar 10"

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)