human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "early prediction and characterization of high impact world events using social media"):
  Abstract: On-line social networks publish information about an enormous volume of real-world events almost instantly, becoming a primary source for breaking news. Many of the events reported in social media can be of high-impact to society, such as important political decisions, natural disasters and terrorist actions, but might go unnoticed in their early stages due to the overload of other information. We ask, is it possible to clearly and quickly identify which of these news events are going to have substantial impact before they actually become a trend in the network? We investigate real-world news discussed on Twitter for approximately 1 year, consisting of 5, 234 news events that are composed of 43 million messages. We show that using just the first 5% of the events’ lifetime evolution, we are able to predict with high precision the top 8% that have the most impact. We observe that events that have high impact present unique characteristics in terms of how they are adopted by the network and that these qualities are independent of the event’s size and scope. As a consequence, high impact news events are naturally filtered by the social network, engaging users early on, much before they are brought to the mainstream audience. Social media has become a primary source of breaking news information for millions of users all over the world (8). On-line social networks along with mobile internet devices have crowdsourced the task of disseminating real-time information. As a result, both news media and news consumers have become inundated with much more information than they can process. To handle this data overload, it is important to find ways to quickly filter information that has the potential of creating an impact on society. Fast identification of high-impact news events, at both Corresponding author: jkalyana@ucsd.edu. JK and GL acknowledge the support of Yahoo Faculty Research Engagement Program.

Includes members:
  Member "codebook based audio feature representation for music information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network")
  Member "characterizing the life cycle of online news stories using social media reactions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators")
  Member "exploring text virality in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "causal effects of brevity on style and success in social media")
  Member "using a model of social dynamics to predict popularity of news":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "predicting the popularity of online content":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online convex optimization for caching networks")

Question: Please tell me which class the source paper 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)