human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "detection of violent extremists in social media"
  Abstract: The ease of use of the Internet has enabled violent extremists such as the Islamic State of Iraq and Syria (ISIS) to easily reach large audience, build personal relationships and increase recruitment. Social media are primarily based on the reports they receive from their own users to mitigate the problem. Despite efforts of social media in suspending many accounts, this solution is not guaranteed to be effective, because not all extremists are caught this way, or they can simply return with another account or migrate to other social networks. In this paper, we design an automatic detection scheme that using as little as three groups of information related to usernames, profile, and textual content of users, determines whether or not a given username belongs to an extremist user. We first demonstrate that extremists are inclined to adopt usernames that are similar to the ones that their like-minded have adopted in the past. We then propose a detection framework that deploys features which are highly indicative of potential online extremism. Results on a real-world ISIS-related dataset from Twitter demonstrate the effectiveness of the methodology in identifying extremist users.

Connected through hyperedge HE-1 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts"):
  with members:
    - "privacy aware recommendation with private attribute protection using adversarial learning"
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "signed link prediction with sparse data the role of personality information"
    - "detecting pathogenic social media accounts without content or network structure"
    - "similar but different exploiting users congruity for recommendation systems"
    - "predicting factuality of reporting and bias of news media sources"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"

Connected through hyperedge HE-2 (induced by source paper "privacy aware recommendation with private attribute protection using adversarial learning"):
  with members:
    - "less is more semi supervised causal inference for detecting pathogenic users in social media"
    - "i am not what i write privacy preserving text representation learning"
    - "hawkes process for understanding the influence of pathogenic social media accounts"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"

Connected through hyperedge HE-3 (induced by source paper "less is more semi supervised causal inference for detecting pathogenic users in social media"):
  with members:
    - "an end to end framework to identify pathogenic social media accounts on twitter"
    - "detecting pathogenic social media accounts without content or network structure"
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "deep neural networks for bot detection"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"

Question: Please tell me which class the center node 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)