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: "self attention a better building block for sentiment analysis neural network classifiers"
  Abstract: Sentiment Analysis has seen much progress in the past two decades. For the past few years, neural network approaches, primarily RNNs and CNNs, have been the most successful for this task. Recently, a new category of neural networks, self-attention networks (SANs), have been created which utilizes the attention mechanism as the basic building block. Self-attention networks have been shown to be effective for sequence modeling tasks, while having no recurrence or convolutions. In this work we explore the effectiveness of the SANs for sentiment analysis. We demonstrate that SANs are superior in performance to their RNN and CNN counterparts by comparing their classification accuracy on six datasets as well as their model characteristics such as training speed and memory consumption. Finally, we explore the effects of various SAN modifications such as multi-head attention as well as two methods of incorporating sequence position information into SANs.

Connected through hyperedge HE-1 (induced by source paper "sentiment analysis with contextual embeddings and self attention"):
  with members:
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"
    - "a structured self attentive sentence embedding"
    - "attentive pooling networks"

Connected through hyperedge HE-2 (induced by source paper "improving sentiment analysis with multi task learning of negation"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "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"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"

Connected through hyperedge HE-3 (induced by source paper "an attentive survey of attention models"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "targeted sentiment classification with attentional encoder network"
    - "attention please a critical review of neural attention models in natural language processing"
    - "attention models in graphs a survey"
    - "dynamic meta embeddings for improved sentence representations"
    - "self attentive sequential recommendation"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis is not solved assessing and probing sentiment classification"):
  with members:
    - "semeval 2013 task 2 sentiment analysis in twitter"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "ltg oslo hierarchical multi task network the importance of negation for document level sentiment in spanish"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "when is multitask learning effective semantic sequence prediction under varying data conditions"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "multi task domain adaptation for sequence tagging"
    - "improved semantic representations from tree structured long short term memory networks"
    - "convolutional neural networks for sentence classification"
    - "natural language processing almost from scratch"
    - "thumbs up sentiment classification using machine learning techniques"

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)