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: "plusemo2vec at semeval 2018 task 1 exploiting emotion knowledge from emoji and hashtags"
  Abstract: This paper describes our system that has been submitted to SemEval-2018 Task 1: Affect in Tweets (AIT) to solve five subtasks. We focus on modeling both sentence and word level representations of emotion inside texts through large distantly labeled corpora with emojis and hashtags. We transfer the emotional knowledge by exploiting neural network models as feature extractors and use these representations for traditional machine learning models such as support vector regression (SVR) and logistic regression to solve the competition tasks. Our system is placed among the Top3 for all subtasks we participated.

Connected through hyperedge HE-1 (induced by source paper "related tasks can share a multi task framework for affective language"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "an overview of multi task learning in deep neural networks"
    - "emotion intensities in tweets"

Connected through hyperedge HE-2 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning"):
  with members:
    - "caire an end to end empathetic chatbot"
    - "a novel repetition normalized adversarial reward for headline generation"
    - "deep communicating agents for abstractive summarization"
    - "improving abstraction in text summarization"
    - "the clickbait challenge 2017 towards a regression model for clickbait strength"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "emo2vec learning generalized emotion representation by multi task training"

Connected through hyperedge HE-3 (induced by source paper "incorporating word and subword units in unsupervised machine translation using language model rescoring"):
  with members:
    - "extract and edit an alternative to back translation for unsupervised neural machine translation"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "an analysis of neural language modeling at multiple scales"
    - "phrase based neural unsupervised machine translation"
    - "are all languages equally hard to language model"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "gated recurrent neural network approach for multilabel emotion detection in microblogs"):
  with members:
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "ntua slp at semeval 2018 task 1 predicting affective content in tweets with deep attentive rnns and transfer learning"
    - "image captioning with semantic attention"
    - "character level convolutional networks for text classification"
    - "effective approaches to attention based neural machine translation"
    - "improved semantic representations from tree structured long short term memory networks"
    - "stacked attention networks for image question answering"

Connected through hyperedge HE-5 (induced by source paper "char rnn and active learning for hashtag segmentation"):
  with members:
    - "an end to end neural natural language interface for databases"
    - "universal word segmentation implementation and interpretation"
    - "deep active learning for named entity recognition"
    - "a gap based framework for chinese word segmentation via very deep convolutional networks"
    - "attention is all you need"
    - "fast and accurate neural word segmentation for chinese"
    - "neural word segmentation learning for chinese"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "caire_hkust at semeval 2019 task 3 hierarchical attention for dialogue emotion classification"):
  with members:
    - "universal transformers"
    - "attnconvnet at semeval 2018 task 1 attention based convolutional neural networks for multi label emotion classification"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "deep contextualized word representations"
    - "attention based lstm for psychological stress detection from spoken language using distant supervision"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-7 (induced by source paper "sharing emotions at scale the vent dataset"):
  with members:
    - "twitter sentiment analysis system"
    - "nrc canada building the state of the art in sentiment analysis of tweets"

Connected through hyperedge HE-8 (induced by source paper "towards empathetic open domain conversation models a new benchmark and dataset"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "multi task deep neural networks for natural language understanding"
    - "learning semantic textual similarity from conversations"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "training millions of personalized dialogue agents"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "polite dialogue generation without parallel data"

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)