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 "breaking disasters predicting and characterizing the global news value of natural and man made disasters"):
  Abstract: Due to their often unexpected nature, natural and man-made disasters are difficult to monitor and detect for journalists and disaster management response teams. Journalists are increasingly relying on signals from social media to detect such stories in their early stage of development. Twitter, which features a vast network of local news outlets, is a major source of early signal for disaster detection. Journalists who work for global desks often follow these sources via Twitter's lists, but have to comb through thousands of small-scale or low-impact stories to find events that may be globally relevant. These are events that have a large scope, high impact, or potential geo-political relevance. We propose a model for automatically identifying events from local news sources that may break on a global scale within the next 24 hours. The results are promising and can be used in a predictive setting to help journalists manage their sources more effectively, or in a descriptive manner to analyze media coverage of disasters. Through the feature evaluation process, we also address the question: "what makes a disaster event newsworthy on a global scale?" As part of our data collection process, we have created a list of local sources of disaster/accident news on Twitter, which we have made publicly available.

Includes members:
  Member "finding eyewitness tweets during crises":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding the radical mind identifying signals to detect extremist content on twitter")
  Member "understanding news geography and major determinants of global news coverage of disasters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "measuring understanding and classifying news media sympathy on twitter after crisis events")

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)