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 "abusive language detection in online conversations by combining content and graph based features"):
  Abstract: In recent years, online social networks have allowed world-wide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content- and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%.

Includes members:
  Member "conversational networks for automatic online moderation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "neural character based composition models for abuse detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "impact of content features for automatic online abuse detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "conversational networks for automatic online moderation")
  Member "deceiving google s perspective api built for detecting toxic comments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "graph based features for automatic online abuse detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conversational networks for automatic online moderation")
  Member "deep learning for user comment moderation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "ex machina personal attacks seen at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multilingual twitter corpus and baselines for evaluating demographic bias in hate speech recognition")

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.IR(Information Retrieval)