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: "hate is not binary studying abusive behavior of gamergate on twitter"
  Abstract: Over the past few years, online bullying and aggression have become increasingly prominent, and manifested in many different forms on social media. However, there is little work analyzing the characteristics of abusive users and what distinguishes them from typical social media users. In this paper, we start addressing this gap by analyzing tweets containing a great large amount of abusiveness. We focus on a Twitter dataset revolving around the Gamergate controversy, which led to many incidents of cyberbullying and cyberaggression on various gaming and social media platforms. We study the properties of the users tweeting about Gamergate, the content they post, and the differences in their behavior compared to typical Twitter users. #R##N#We find that while their tweets are often seemingly about aggressive and hateful subjects, "Gamergaters" do not exhibit common expressions of online anger, and in fact primarily differ from typical users in that their tweets are less joyful. They are also more engaged than typical Twitter users, which is an indication as to how and why this controversy is still ongoing. Surprisingly, we find that Gamergaters are less likely to be suspended by Twitter, thus we analyze their properties to identify differences from typical users and what may have led to their suspension. We perform an unsupervised machine learning analysis to detect clusters of users who, though currently active, could be considered for suspension since they exhibit similar behaviors with suspended users. Finally, we confirm the usefulness of our analyzed features by emulating the Twitter suspension mechanism with a supervised learning method, achieving very good precision and recall.

Connected through hyperedge HE-1 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities"):
  with members:
    - "characterizing the use of images by state sponsored troll accounts on twitter"
    - "characterizing the use of images in state sponsored information warfare operations by russian trolls on twitter"
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "a quantitative approach to understanding online antisemitism"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "disinformation warfare understanding state sponsored trolls on twitter and their influence on the web"
    - "mobilizing the trump train understanding collective action in a political trolling community"
    - "false information on web and social media a survey"

Connected through hyperedge HE-2 (induced by source paper "detecting cyberbullying and cyberaggression in social media"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "poised spotting twitter spam off the beaten paths"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "a longitudinal measurement study of 4chan s politically incorrect forum and its effect on the web"
    - "deep residual learning for image recognition"
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-3 (induced by source paper "characterizing and detecting hateful users on twitter"):
  with members:
    - "the wisdom of polarized crowds"
    - "understanding abuse a typology of abusive language detection subtasks"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "inductive representation learning on large graphs"
    - "deep learning for hate speech detection in tweets"
    - "representation learning on graphs methods and applications"
    - "detecting the hate code on social media"

Connected through hyperedge HE-4 (induced by source paper "you know what to do proactive detection of youtube videos targeted by coordinated hate attacks"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "the effect of extremist violence on hateful speech online"
    - "community interaction and conflict on the web"
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "opinion conflicts an effective route to detect incivility in twitter"
    - "mean birds detecting aggression and bullying on twitter"
    - "poised spotting twitter spam off the beaten paths"

Connected through hyperedge HE-5 (induced by source paper "opinion conflicts an effective route to detect incivility in twitter"):
  with members:
    - "peer to peer hate hate speech instigators and their targets"
    - "hate lingo a target based linguistic analysis of hate speech in social media"
    - "mean birds detecting aggression and bullying on twitter"
    - "measuring gamergate a tale of hate sexism and bullying"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "analyzing the targets of hate in online social media"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-6 (induced by source paper "peer to peer hate hate speech instigators and their targets"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "a web of hate tackling hateful speech in online social spaces"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "analyzing the targets of hate in online social media"
    - "antisocial behavior in online discussion communities"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

Connected through hyperedge HE-7 (induced by source paper "understanding web archiving services and their mis use on social media"):
  with members:
    - "consumers and curators browsing and voting patterns on reddit"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the dawn of today s popular domains a study of the archived german web over 18 years"
    - "gone in six characters short urls considered harmful for cloud services"
    - "adscape harvesting and analyzing online display ads"
    - "who and what links to the internet archive"
    - "how much of the web is archived"

Connected through hyperedge HE-8 (induced by source paper "mean birds detecting aggression and bullying on twitter"):
  with members:
    - "measuring gamergate a tale of hate sexism and bullying"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "the social world of content abusers in community question answering"
    - "towards understanding cyberbullying behavior in a semi anonymous social network"
    - "efficient estimation of word representations in vector space"
    - "smote synthetic minority over sampling technique"

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.SI(Social and Information Networks)