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: "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"
  Abstract: Over the years, the Web has shrunk the world, allowing individuals to share viewpoints with many more people than they are able to in real life. At the same time, however, it has also enabled anti-social and toxic behavior to occur at an unprecedented scale. Video sharing platforms like YouTube receive uploads from millions of users, covering a wide variety of topics and allowing others to comment and interact in response. Unfortunately, these communities are periodically plagued with aggression and hate attacks. In particular, recent work has showed how these attacks often take place as a result of "raids," i.e., organized efforts coordinated by ad-hoc mobs from third-party communities. #R##N#Despite the increasing relevance of this phenomenon, online services often lack effective countermeasures to mitigate it. Unlike well-studied problems like spam and phishing, coordinated aggressive behavior both targets and is perpetrated by humans, making defense mechanisms that look for automated activity unsuitable. Therefore, the de-facto solution is to reactively rely on user reports and human reviews. In this paper, we propose an automated solution to identify videos that are likely to be targeted by coordinated harassers. First, we characterize and model YouTube videos along several axes (metadata, audio transcripts, thumbnails) based on a ground truth dataset of raid victims. Then, we use an ensemble of classifiers to determine the likelihood that a video will be raided with high accuracy (AUC up to 94%). Overall, our work paves the way for providing video platforms like YouTube with proactive systems to detect and mitigate coordinated hate attacks.

Connected through hyperedge HE-1 (induced by source paper "raiders of the lost kek 3 5 years of augmented 4chan posts from the politically incorrect board"):
  with members:
    - "and we will fight for our race a measurement study of genetic testing conversations on reddit and 4chan"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "whatsapp doc a first look at whatsapp public group data"
    - "on the origins of memes by means of fringe web communities"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"

Connected through hyperedge HE-2 (induced by source paper "understanding the incel community on youtube"):
  with members:
    - "auditing radicalization pathways on youtube"
    - "disturbed youtube for kids characterizing and detecting inappropriate videos targeting young children"
    - "a quantitative approach to understanding online antisemitism"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "bridging information security and environmental criminology research to better mitigate cybercrime"):
  with members:
    - "a unified deep learning architecture for abuse detection"
    - "mean birds detecting aggression and bullying on twitter"
    - "what s in a name understanding profile name reuse on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "traveling the silk road a measurement analysis of a large anonymous online marketplace"

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.CY(Computers and Society)