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: "deep feelings a massive cross lingual study on the relation between emotions and virality"
  Abstract: This article provides a comprehensive investigation on the relations between virality of news articles and the emotions they are found to evoke. Virality, in our view, is a phenomenon with many facets, i.e. under this generic term several different effects of persuasive communication are comprised. By exploiting a high-coverage and bilingual corpus of documents containing metrics of their spread on social networks as well as a massive affective annotation provided by readers, we present a thorough analysis of the interplay between evoked emotions and viral facets. We highlight and discuss our findings in light of a cross-lingual approach: while we discover differences in evoked emotions and corresponding viral effects, we provide preliminary evidence of a generalized explanatory model rooted in the deep structure of emotions: the Valence-Arousal-Dominance (VAD) circumplex. We find that viral facets appear to be consistently affected by particular VAD configurations, and these configurations indicate a clear connection with distinct phenomena underlying persuasive communication.

Connected through hyperedge HE-1 (induced by source paper "recognizing developers emotions while programming"):
  with members:
    - "a replication study on code comprehension and expertise using lightweight biometric sensors"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "the impact of automated parameter optimization on defect prediction models"
    - "whom are you going to call determinants of mentions in github discussions"
    - "emotion detection using noninvasive low cost sensors"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"

Connected through hyperedge HE-2 (induced by source paper "depechemood a bilingual emotion lexicon built through simple yet powerful techniques"):
  with members:
    - "word affect intensities"
    - "wassa 2017 shared task on emotion intensity"
    - "analysis of italian word embeddings"
    - "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

Connected through hyperedge HE-3 (induced by source paper "fortia fbk at semeval 2017 task 5 bullish or bearish inferring sentiment towards brands from financial news headlines"):
  with members:
    - "word embeddings and their use in sentence classification tasks"
    - "sentiwords deriving a high precision and high coverage lexicon for sentiment analysis"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "mining valence arousal and dominance possibilities for detecting burnout and productivity"):
  with members:
    - "understanding the affect of developers theoretical background and guidelines for psychoempirical software engineering"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"

Connected through hyperedge HE-5 (induced by source paper "understanding image virality"):
  with members:
    - "becoming the expert interactive multi class machine teaching"
    - "cloudcv large scale distributed computer vision as a cloud service"
    - "deep fragment embeddings for bidirectional image sentence mapping"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "exploring image virality in google plus"
    - "a scalable heuristic for viral marketing under the tipping model"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.SI(Social and Information Networks)