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: "from once upon a time to happily ever after tracking emotions in novels and fairy tales"
  Abstract: Today we have access to unprecedented amounts of literary texts. However, search still relies heavily on key words. In this paper, we show how sentiment analysis can be used in tandem with effective visualizations to quantify and track emotions in both individual books and across very large collections. We introduce the concept of emotion word density, and using the Brothers Grimm fairy tales as example, we show how collections of text can be organized for better search. Using the Google Books Corpus we show how to determine an entity's emotion associations from co-occurring words. Finally, we compare emotion words in fairy tales and novels, to show that fairy tales have a much wider range of emotion word densities than novels.

Connected through hyperedge HE-1 (induced by source paper "shaping the narrative arc an information theoretic approach to collaborative dialogue"):
  with members:
    - "predicting movie genres based on plot summaries"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "generating wikipedia by summarizing long sequences"
    - "smart conversational agents for reminiscence"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "toward controlled generation of text"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-2 (induced by source paper "rating for parents predicting children suitability rating for movies based on language of the movies"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "one step and two step classification for abusive language detection on twitter"
    - "adam a method for stochastic optimization"
    - "towards understanding cyberbullying behavior in a semi anonymous social network"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "crowdsourcing and validating event focused emotion corpora for german and english"):
  with members:
    - "distant supervision for emotion detection using facebook reactions"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-4 (induced by source paper "a survey on sentiment and emotion analysis for computational literary studies"):
  with members:
    - "frowning frodo wincing leia and a seriously great friendship learning to classify emotional relationships of fictional characters"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "an analysis of emotion communication channels in fan fiction towards emotional storytelling"
    - "deep learning for sentiment analysis a survey"
    - "the evolution of sentiment analysis a review of research topics venues and top cited papers"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "wassa 2017 shared task on emotion intensity"
    - "the emotional arcs of stories are dominated by six basic shapes"

Connected through hyperedge HE-5 (induced by source paper "letting emotions flow success prediction by modeling the flow of emotions in books"):
  with members:
    - "mpst a corpus of movie plot synopses with tags"
    - "self normalizing neural networks"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "teaching machines to read and comprehend"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "folksonomication predicting tags for movies from plot synopses using emotion flow encoded neural network"):
  with members:
    - "mpst a corpus of movie plot synopses with tags"
    - "the emotional arcs of stories are dominated by six basic shapes"
    - "hierarchical attention networks"
    - "you re mr lebowski i m the dude inducing address term formality in signed social networks"
    - "recurrent models of visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "multiple object recognition with visual attention"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-7 (induced by source paper "reflections on sentiment opinion analysis"):
  with members:
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "twitter mood predicts the stock market"
    - "finding deceptive opinion spam by any stretch of the imagination"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"
    - "thumbs up sentiment classification using machine learning techniques"

Connected through hyperedge HE-8 (induced by source paper "tracking sentiment in mail how genders differ on emotional axes"):
  with members:
    - "measuring the happiness of large scale written expression songs blogs and presidents"
    - "measuring praise and criticism inference of semantic orientation from association"

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)