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 "less is more semi supervised causal inference for detecting pathogenic users in social media"):
  Abstract: Recent years have witnessed a surge of manipulation of public opinion and political events by malicious social media actors. These users are referred to as "Pathogenic Social Media (PSM)" accounts. PSMs are key users in spreading misinformation in social media to viral proportions. These accounts can be either controlled by real users or automated bots. Identification of PSMs is thus of utmost importance for social media authorities. The burden usually falls to automatic approaches that can identify these accounts and protect social media reputation. However, lack of sufficient labeled examples for devising and training sophisticated approaches to combat these accounts is still one of the foremost challenges facing social media firms. In contrast, unlabeled data is abundant and cheap to obtain thanks to massive user-generated data. In this paper, we propose a semi-supervised causal inference PSM detection framework, SemiPsm, to compensate for the lack of labeled data. In particular, the proposed method leverages unlabeled data in the form of manifold regularization and only relies on cascade information. This is in contrast to the existing approaches that use exhaustive feature engineering (e.g., profile information, network structure, etc.). Evidence from empirical experiments on a real-world ISIS-related dataset from Twitter suggests promising results of utilizing unlabeled instances for detecting PSMs.

Includes members:
  Member "an end to end framework to identify pathogenic social media accounts on twitter":
    also connected through other hyperedges:
      (none)
  Member "detection of violent extremists 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 "detecting pathogenic social media accounts without content or network structure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "deep neural networks for bot detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "early identification of pathogenic social media accounts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")

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)