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 "a feature driven approach for identifying pathogenic social media accounts"):
  Abstract: Over the past few years, we have observed different media outlets' attempts to shift public opinion by framing information to support a narrative that facilitate their goals. Malicious users referred to as "pathogenic social media" (PSM) accounts are more likely to amplify this phenomena by spreading misinformation to viral proportions. Understanding the spread of misinformation from account-level perspective is thus a pressing problem. In this work, we aim to present a feature-driven approach to detect PSM accounts in social media. Inspired by the literature, we set out to assess PSMs from three broad perspectives: (1) user-related information (e.g., user activity, profile characteristics), (2) source-related information (i.e., information linked via URLs shared by users) and (3) content-related information (e.g., tweets characteristics). For the user-related information, we investigate malicious signals using causality analysis (i.e., if user is frequently a cause of viral cascades) and profile characteristics (e.g., number of followers, etc.). For the source-related information, we explore various malicious properties linked to URLs (e.g., URL address, content of the associated website, etc.). Finally, for the content-related information, we examine attributes (e.g., number of hashtags, suspicious hashtags, etc.) from tweets posted by users. Experiments on real-world Twitter data from different countries demonstrate the effectiveness of the proposed approach in identifying PSM users.

Includes members:
  Member "privacy aware recommendation with private attribute protection using adversarial learning":
    also connected through other hyperedges:
      (none)
  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 "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "hawkes process for understanding the influence of pathogenic social media accounts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "signed link prediction with sparse data the role of personality information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social science guided feature engineering a novel approach to signed link analysis")
  Member "detection of violent extremists in social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "detecting pathogenic social media accounts without content or network structure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media")
  Member "similar but different exploiting users congruity for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning")
  Member "predicting factuality of reporting and bias of news media sources":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting toxicity in news articles application to bulgarian")

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)