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: "semeval 2013 task 2 sentiment analysis in twitter"
  Abstract: In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The best-performing team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.

Connected through hyperedge HE-1 (induced by source paper "improving sentiment analysis with multi task learning of negation"):
  with members:
    - "bert post training for review reading comprehension and aspect based sentiment analysis"
    - "a multi sentiment resource enhanced attention network for sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "universal language model fine tuning for text classification"
    - "self attention a better building block for sentiment analysis neural network classifiers"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"

Connected through hyperedge HE-2 (induced by source paper "sentiment analysis is not solved assessing and probing sentiment classification"):
  with members:
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reducing gender bias in abusive language detection"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "self attention a better building block for sentiment analysis neural network classifiers"

Connected through hyperedge HE-3 (induced by source paper "sentiment tagging with partial labels using modular architectures"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "learning to parse and translate improves neural machine translation"
    - "learning to reason end to end module networks for visual question answering"
    - "multitask learning with low level auxiliary tasks for encoder decoder based speech recognition"
    - "empower sequence labeling with task aware neural language model"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "learning to compose neural networks for question answering"

Connected through hyperedge HE-4 (induced by source paper "machine translation for machines the sentiment classification use case"):
  with members:
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "can neural machine translation be improved with user feedback"
    - "a study of reinforcement learning for neural machine translation"
    - "getting gender right in neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"

Connected through hyperedge HE-5 (induced by source paper "using structured representation and data a hybrid model for negation and sentiment in customer service conversations"):
  with members:
    - "lexicon integrated cnn models with attention for sentiment analysis"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "semeval 2015 task 10 sentiment analysis in twitter"):
  with members:
    - "semeval 2014 task 9 sentiment analysis in twitter"
    - "nrc canada building the state of the art in sentiment analysis of tweets"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-7 (induced by source paper "emo2vec learning generalized emotion representation by multi task training"):
  with members:
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "tensor fusion network for multimodal sentiment analysis"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "learning language representations for typology prediction"
    - "deep multitask learning for semantic dependency parsing"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "multilingual language processing from bytes"

Connected through hyperedge HE-8 (induced by source paper "zero shot sequence labeling transferring knowledge from sentences to tokens"):
  with members:
    - "semi supervised multitask learning for sequence labeling"
    - "semi supervised sequence tagging with bidirectional language models"
    - "neural attention models for sequence classification analysis and application to key term extraction and dialogue act detection"
    - "automatic text scoring using neural networks"
    - "neural architectures for named entity recognition"
    - "a neural attention model for abstractive sentence summarization"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "reasoning about entailment with neural attention"

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)