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 "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.

Includes members:
  Member "what is gab a bastion of free speech or an alt right echo chamber":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere")
  Member "the effect of extremist violence on hateful speech online":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere")
  Member "community interaction and conflict on the web":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "peer to peer hate hate speech instigators and their targets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a benchmark dataset for learning to intervene in online hate speech")
  Member "hate lingo a target based linguistic analysis of hate speech in social media":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for the computational linguistic analysis of dehumanization")
  Member "opinion conflicts an effective route to detect incivility in twitter":
    also connected through other hyperedges:
      (none)
  Member "mean birds detecting aggression and bullying on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "angry birds flock together aggression propagation on social media")
  Member "poised spotting twitter spam off the beaten paths":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook")

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.CY(Computers and Society)