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 "social science guided feature engineering a novel approach to signed link analysis"):
  Abstract: Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article, we center our discussion on a challenging problem of signed link analysis. Signed link analysis faces the problem of data sparsity, i.e. only a small percentage of signed links are given. This problem can even get worse when negative links are much sparser than positive ones as users are inclined more towards positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information, Diffusion of Innovations, and Individual Personality. Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments show the significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.

Includes members:
  Member "less is more semi supervised causal inference for detecting pathogenic users in social media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "hawkes process for understanding the influence of pathogenic social media accounts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "signed link prediction with sparse data the role of personality information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "signed network modeling based on structural balance theory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "balance in signed bipartite networks")
  Member "semi supervised learning for detecting human trafficking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media")

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)