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: "the social world of content abusers in community question answering"
  Abstract: Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83%.

Connected through hyperedge HE-1 (induced by source paper "angry birds flock together aggression propagation on social media"):
  with members:
    - "detecting cyberbullying and cyberaggression in social media"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "opinion maximization in social networks"

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"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "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"

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 "large scale crowdsourcing and characterization of twitter abusive behavior"):
  with members:
    - "like sheep among wolves characterizing hateful users on twitter"
    - "mean birds detecting aggression and bullying on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "detecting hate speech in social media"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "making the most of tweet inherent features for social spam detection on twitter"

Connected through hyperedge HE-6 (induced by source paper "privacy and security in online social networks a survey"):
  with members:
    - "social fingerprinting detection of spambot groups through dna inspired behavioral modeling"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "truetop a sybil resilient system for user influence measurement on twitter"
    - "the rise of social bots"
    - "privacy concerns vs user behavior in community question answering"
    - "special issue on foundations of social computing enabling social applications via decentralized social data management"
    - "online social networks threats and solutions"
    - "sybilbelief a semi supervised learning approach for structure based sybil detection"

Connected through hyperedge HE-7 (induced by source paper "mean birds detecting aggression and bullying on twitter"):
  with members:
    - "measuring gamergate a tale of hate sexism and bullying"
    - "hate is not binary studying abusive behavior of gamergate on twitter"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"
    - "towards understanding cyberbullying behavior in a semi anonymous social network"
    - "efficient estimation of word representations in vector space"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-8 (induced by source paper "measuring gamergate a tale of hate sexism and bullying"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "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"
    - "towards understanding cyberbullying behavior in a semi anonymous social network"

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)