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: "conversational networks for automatic online moderation"
  Abstract: Moderation of user-generated content in an online community is a challenge that has great socio-economic ramifications. However, the costs incurred by delegating this paper to human agents are high. For this reason, an automatic system able to detect abuse in user-generated content is of great interest. There are a number of ways to tackle this problem, but the most commonly seen in practice are word filtering or regular expression matching. The main limitations are their vulnerability to intentional obfuscation on the part of the users, and their context-insensitive nature. Moreover, they are language dependent and may require appropriate corpora for training. In this paper, we propose a system for automatic abuse detection that completely disregards message content. We first extract a conversational network from raw chat logs and characterize it through topological measures. We then use these as features to train a classifier on our abuse detection task. We thoroughly assess our system on a dataset of user comments originating from a French massively multiplayer online game. We identify the most appropriate network extraction parameters and discuss the discriminative power of our features, relatively to their topological and temporal nature. Our method reaches an    $F$   -measure of 83.89 when using the full feature set, improving on existing approaches. With a selection of the most discriminative features, we dramatically cut computing time while retaining the most of the performance (82.65).

Connected through hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection"):
  with members:
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "abusive language detection in online conversations by combining content and graph based features"
    - "all you need is love evading hate speech detection"
    - "neural character based composition models for abuse detection"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "deep learning for user comment moderation"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-2 (induced by source paper "extraction and analysis of fictional character networks a survey"):
  with members:
    - "remembering winter was coming character oriented video summaries of tv series"
    - "remembering winter was coming"
    - "predicting kills in game of thrones using network properties"
    - "multilayer network model of movie script"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "i will be there for you six friends in a clique"
    - "the one comparing narrative social network extraction techniques"
    - "a survey of comics research in computer science"

Connected through hyperedge HE-3 (induced by source paper "abusive language detection in online conversations by combining content and graph based features"):
  with members:
    - "neural character based composition models for abuse detection"
    - "impact of content features for automatic online 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"

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)