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 "wac a corpus of wikipedia conversations for online abuse detection"):
  Abstract: With the spread of online social networks, it is more and more difficult to monitor all the user-generated content. Automating the moderation process of the inappropriate exchange content on Internet has thus become a priority task. Methods have been proposed for this purpose, but it can be challenging to find a suitable dataset to train and develop them. This issue is especially true for approaches based on information derived from the structure and the dynamic of the conversation. In this work, we propose an original framework, based on the Wikipedia Comment corpus, with comment-level abuse annotations of different types. The major contribution concerns the reconstruction of conversations, by comparison to existing corpora, which focus only on isolated messages (i.e. taken out of their conversational context). This large corpus of more than 380k annotated messages opens perspectives for online abuse detection and especially for context-based approaches. We also propose, in addition to this corpus, a complete benchmarking platform to stimulate and fairly compare scientific works around the problem of content abuse detection, trying to avoid the recurring problem of result replication. Finally, we apply two classification methods to our dataset to demonstrate its potential.

Includes members:
  Member "conversational networks for automatic online moderation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "extraction and analysis of fictional character networks a survey")
  Member "trouble on the horizon forecasting the derailment of online conversations as they develop":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "content removal as a moderation strategy compliance and other outcomes in the changemyview community")
  Member "abusive language detection in online conversations by combining content and graph based features":
    also connected through other hyperedges:
      (none)
  Member "all you need is love evading hate speech detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")
  Member "neural character based composition models for abuse detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a gentle introduction to deep learning for graphs")
  Member "wikiconv a corpus of the complete conversational history of a large online collaborative community":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trouble on the horizon forecasting the derailment of online conversations as they develop")
  Member "deceiving google s perspective api built for detecting toxic comments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere")
  Member "deep learning for user comment moderation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning invariant representations of social media users")

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.CL(Computation and Language)