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: "solutions to detect and analyze online radicalization a survey"
  Abstract: Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or Cyber- Hate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organi- zations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps.

Connected through hyperedge HE-1 (induced by source paper "contagion dynamics of extremist propaganda in social networks"):
  with members:
    - "the rise of jihadist propaganda on social networks"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "online human bot interactions detection estimation and characterization"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "the darpa twitter bot challenge"
    - "spider and the flies focused crawling on tumblr to detect hate promoting communities"
    - "network structure and resilience of mafia syndicates"
    - "the rise of social bots"

Connected through hyperedge HE-2 (induced by source paper "predicting online extremism content adopters and interaction reciprocity"):
  with members:
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "spider and the flies focused crawling on tumblr to detect hate promoting communities"
    - "the rise of social bots"
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "applying social media intelligence for predicting and identifying on line radicalization and civil unrest oriented threats"
    - "evolution of online user behavior during a social upheaval"
    - "the digital evolution of occupy wall street"

Connected through hyperedge HE-3 (induced by source paper "applying social media intelligence for predicting and identifying on line radicalization and civil unrest oriented threats"):
  with members:
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "uncovering the wider structure of extreme right communities spanning popular online networks"
    - "early warning analysis for social diffusion events"
    - "an analysis of interactions within and between extreme right communities in social media"

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.IR(Information Retrieval)