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: "social emotion mining techniques for facebook posts reaction prediction"
  Abstract: As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions'. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135.

Connected through hyperedge HE-1 (induced by source paper "measuring the diversity of facebook reactions to research"):
  with members:
    - "jointly learning to detect emotions and predict facebook reactions"
    - "shared feelings understanding facebook reactions to scholarly articles"
    - "community interaction and conflict on the web"
    - "could scientists use altmetric com scores to predict longer term citation counts"
    - "anatomy of scholarly information behavior patterns in the wake of academic social media platforms"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "anger is more influential than joy sentiment correlation in weibo"

Connected through hyperedge HE-2 (induced by source paper "cross lingual data transformation and combination for text classification"):
  with members:
    - "learning word vectors for 157 languages"
    - "revisiting the importance of encoding logic rules in sentiment classification"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "fasttext zip compressing text classification models"
    - "one model to learn them all"
    - "convolutional neural networks for medical diagnosis from admission notes"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-3 (induced by source paper "metoomaastricht building a chatbot to assist survivors of sexual harassment"):
  with members:
    - "safecity understanding diverse forms of sexual harassment personal stories"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "distributed representations of sentences and documents"
    - "word2vec parameter learning explained"
    - "efficient estimation of word representations in vector space"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "shared feelings understanding facebook reactions to scholarly articles"):
  with members:
    - "distant supervision for emotion detection using facebook reactions"
    - "how the scientific community reacts to newly submitted preprints article downloads twitter mentions and citations"

Connected through hyperedge HE-5 (induced by source paper "jointly learning to detect emotions and predict facebook reactions"):
  with members:
    - "distant supervision for emotion detection using facebook reactions"

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.AI(Artificial Intelligence)