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: "leveraging large amounts of weakly supervised data for multi language sentiment classification"
  Abstract: This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weakly-supervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pre-training of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved state-of-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse - but still acceptable - performance when compared to the single language model, while benefiting from better generalization properties across languages.

Connected through hyperedge HE-1 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition"):
  with members:
    - "mitigating gender bias in natural language processing literature review"
    - "huggingface s transformers state of the art natural language processing"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "racial bias in hate speech and abusive language detection datasets"
    - "how to fine tune bert for text classification"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "counterfactual fairness in text classification through robustness"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "unisent universal adaptable sentiment lexica for 1000 languages"):
  with members:
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "past present future a computational investigation of the typology of tense in 1000 languages"
    - "word translation without parallel data"
    - "inducing domain specific sentiment lexicons from unlabeled corpora"
    - "enriching word vectors with subword information"
    - "comparing fifty natural languages and twelve genetic languages using word embedding language divergence weld as a quantitative measure of language distance"
    - "ultradense word embeddings by orthogonal transformation"
    - "statistically significant detection of linguistic change"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis with genetically evolved gaussian kernels"):
  with members:
    - "learning structural kernels for natural language processing"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "evolution of covariance functions for gaussian process regression using genetic programming"
    - "genetic programming for kernel based learning with co evolving subsets selection"

Connected through hyperedge HE-4 (induced by source paper "context mover s distance barycenters optimal transport of contexts for building representations"):
  with members:
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "word mover s embedding from word2vec to document embedding"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "not to cry wolf distantly supervised multitask learning in critical care"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "granger causal attentive mixtures of experts learning important features with neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"
    - "multi task self supervised visual learning"
    - "a reinforcement learning approach to weaning of mechanical ventilation in intensive care units"

Connected through hyperedge HE-6 (induced by source paper "ermes emoji powered representation learning for cross lingual sentiment classification"):
  with members:
    - "deep learning for sentiment analysis a survey"
    - "through a gender lens learning usage patterns of emojis from large scale android users"
    - "spice up your chat the intentions and sentiment effects of using emoji"
    - "multitask learning for fine grained twitter sentiment analysis"
    - "tensorflow a system for large scale machine learning"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-7 (induced by source paper "deepemo learning and enriching pattern based emotion representations"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "enriching complex networks with word embeddings for detecting mild cognitive impairment from speech transcripts"
    - "emotion intensities in tweets"
    - "emoji2vec learning emoji representations from their description"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "real time monitoring of social media and digital press"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "elixa a modular and flexible absa platform"
    - "towards real time country level location classification of worldwide tweets"
    - "semi supervised sequence learning"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "distributed representations of words and phrases and their compositionality"

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)