human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "moralstrength exploiting a moral lexicon and embedding similarity for moral foundations prediction"):
  Abstract: Abstract   Moral rhetoric plays a fundamental role in how we perceive and interpret the information we receive, greatly influencing our decision-making process. Especially when it comes to controversial social and political issues, our opinions and attitudes are hardly ever based on evidence alone. The Moral Foundations Dictionary (MFD) was developed to operationalize moral values in the text. In this study, we present MoralStrength, a lexicon of approximately 1,000 lemmas, obtained as an extension of the Moral Foundations Dictionary, based on WordNet synsets. Moreover, for each lemma it provides with a crowdsourced numeric assessment of Moral Valence, indicating the strength with which a lemma is expressing the specific value. We evaluated the predictive potentials of this moral lexicon, defining three utilization approaches of increased complexity, ranging from lemmas’ statistical properties to a deep learning approach of word embeddings based on semantic similarity. Logistic regression models trained on the features extracted from MoralStrength, significantly outperformed the current state-of-the-art, reaching an F1-score of 87.6% over the previous 62.4% (   p   -value       0  .  01    ), and an average F1-Score of 86.25% over six different datasets. Such findings pave the way for further research, allowing for an in-depth understanding of moral narratives in text for a wide range of social issues.

Includes members:
  Member "human values and attitudes towards vaccination in social media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "predicting demographics moral foundations and human values from digital behaviours":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "effect of values and technology use on exercise implications for personalized behavior change interventions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "facebook ads as a demographic tool to measure the urban rural divide")
  Member "depechemood a bilingual emotion lexicon built through simple yet powerful techniques":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "affective computing for large scale heterogeneous multimedia data a survey")
  Member "automated hate speech detection and the problem of offensive language":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "quantifying moral foundations from various topics on twitter conversations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inference of personal attributes from tweets using machine learning")
  Member "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences")

Question: Please tell me which class the source paper 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)