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: "to act or react investigating proactive strategies for online community moderation"
  Abstract: Reddit administrators have generally struggled to prevent or contain such discourse for several reasons including: (1) the inability for a handful of human administrators to track and react to millions of posts and comments per day and (2) fear of backlash as a consequence of administrative decisions to ban or quarantine hateful communities. Consequently, as shown in our background research, administrative actions (community bans and quarantines) are often taken in reaction to media pressure following offensive discourse within a community spilling into the real world with serious consequences. In this paper, we investigate the feasibility of proactive moderation on Reddit -- i.e., proactively identifying communities at risk of committing offenses that previously resulted in bans for other communities. Proactive moderation strategies show promise for two reasons: (1) they have potential to narrow down the communities that administrators need to monitor for hateful content and (2) they give administrators a scientific rationale to back their administrative decisions and interventions. Our work shows that communities are constantly evolving in their user base and topics of discourse and that evolution into hateful or dangerous (i.e., considered bannable by Reddit administrators) communities can often be predicted months ahead of time. This makes proactive moderation feasible. Further, we leverage explainable machine learning to help identify the strongest predictors of evolution into dangerous communities. This provides administrators with insights into the characteristics of communities at risk becoming dangerous or hateful. Finally, we investigate, at scale, the impact of participation in hateful and dangerous subreddits and the effectiveness of community bans and quarantines on the behavior of members of these communities.

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)