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 "signed link prediction with sparse data the role of personality information"):
  Abstract: Predicting signed links in social networks often faces the problem of signed link data sparsity, i.e., only a small percentage of signed links are given. The problem is exacerbated when the number of negative links is much smaller than that of positive links. Boosting signed link prediction necessitates additional information to compensate for data sparsity. According to psychology theories, one rich source of such information is user's personality such as optimism and pessimism that can help determine her propensity in establishing positive and negative links. In this study, we investigate how personality information can be obtained, and if personality information can help alleviate the data sparsity problem for signed link prediction. We propose a novel signed link prediction model that enables empirical exploration of user personality via social media data. We evaluate our proposed model on two datasets of real-world signed link networks. The results demonstrate the complementary role of personality information in the signed link prediction problem. Experimental results also indicate the effectiveness of different levels of personality information for signed link data sparsity problem.

Includes members:
  Member "hawkes process for understanding the influence of pathogenic social media accounts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "privacy in social media identification mitigation and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "similar but different exploiting users congruity for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "early identification of pathogenic social media accounts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "securing social media user data an adversarial approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "semi supervised learning for detecting human trafficking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "signed link analysis in social media networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")

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.SI(Social and Information Networks)