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: "understanding news geography and major determinants of global news coverage of disasters"
  Abstract: In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as the number of population, the political stability, the damage, and more, are well aligned with a series of previous research. Yet, strong regionalism we found in news geography highlights the necessity of the comprehensive dataset for the study of global news coverage.

Connected through hyperedge HE-1 (induced by source paper "measuring understanding and classifying news media sympathy on twitter after crisis events"):
  with members:
    - "are you charlie or ahmed cultural pluralism in charlie hebdo response on twitter"
    - "a primer on neural network models for natural language processing"
    - "controversy and sentiment in online news"
    - "convolutional neural networks for sentence classification"
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "efficient estimation of word representations in vector space"
    - "adadelta an adaptive learning rate method"
    - "good friends bad news affect and virality in twitter"

Connected through hyperedge HE-2 (induced by source paper "breaking disasters predicting and characterizing the global news value of natural and man made disasters"):
  with members:
    - "finding eyewitness tweets during crises"

Connected through hyperedge HE-3 (induced by source paper "revealing the hidden patterns of news photos analysis of millions of news photos using gdelt and deep learning based vision apis"):
  with members:
    - "two tales of the world comparison of widely used world news datasets gdelt and eventregistry"
    - "breaking the news first impressions matter on online news"

Connected through hyperedge HE-4 (induced by source paper "breaking the news first impressions matter on online news"):
  with members:
    - "characterizing the life cycle of online news stories using social media reactions"
    - "how community feedback shapes user behavior"
    - "comparing and combining sentiment analysis methods"

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.CY(Computers and Society)