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: "impact of content features for automatic online abuse detection"
  Abstract: Online communities have gained considerable importance in recent years due to the increasing number of people connected to the Internet. Moderating user content in online communities is mainly performed manually, and reducing the workload through automatic methods is of great financial interest for community maintainers. Often, the industry uses basic approaches such as bad words filtering and regular expression matching to assist the moderators. In this article, we consider the task of automatically determining if a message is abusive. This task is complex since messages are written in a non-standardized way, including spelling errors, abbreviations, community-specific codes... First, we evaluate the system that we propose using standard features of online messages. Then, we evaluate the impact of the addition of pre-processing strategies, as well as original specific features developed for the community of an online in-browser strategy game. We finally propose to analyze the usefulness of this wide range of features using feature selection. This work can lead to two possible applications: 1) automatically flag potentially abusive messages to draw the moderator's attention on a narrow subset of messages ; and 2) fully automate the moderation process by deciding whether a message is abusive without any human intervention.

Connected through hyperedge HE-1 (induced by source paper "conversational networks for automatic online moderation"):
  with members:
    - "one step and two step classification for abusive language detection on twitter"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "graph based features for automatic online abuse detection"
    - "deep learning for user comment moderation"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "antisocial behavior in online discussion communities"
    - "quantifying controversy in social media"
    - "identifying the community roles of social capitalists in the twitter network"

Connected through hyperedge HE-2 (induced by source paper "context sensitive malicious spelling error correction"):
  with members:
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "neural networks for text correction and completion in keyboard decoding"
    - "geometry of compositionality"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-3 (induced by source paper "abusive language detection in online conversations by combining content and graph based features"):
  with members:
    - "conversational networks for automatic online moderation"
    - "neural character based composition models for abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "graph based features for automatic online abuse detection"
    - "deep learning for user comment moderation"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-4 (induced by source paper "detecting offensive content in open domain conversations using two stage semi supervision"):
  with members:
    - "conversational ai the science behind the alexa prize"
    - "automated hate speech detection and the problem of offensive language"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"

Connected through hyperedge HE-5 (induced by source paper "graph based features for automatic online abuse detection"):
  with members:
    - "deceiving google s perspective api built for detecting toxic comments"
    - "antisocial behavior in online discussion communities"
    - "investigating substructures in goal oriented online communities case study of ubuntu irc"

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)