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: "tracking employment shocks using mobile phone data"
  Abstract: Can data from mobile phones be used to observe economic shocks and their consequences at multiple scales? Here we present novel methods to detect mass layoffs, identify individuals affected by them, and predict changes in aggregate unemployment rates using call detail records (CDRs) from mobile phones. Using the closure of a large manufacturing plant as a case study, we first describe a structural break model to correctly detect the date of a mass layoff and estimate its size. We then use a Bayesian classification model to identify affected individuals by observing changes in calling behavior following the plant's closure. For these affected individuals, we observe significant declines in social behavior and mobility following job loss. Using the features identified at the micro level, we show that the same changes in these calling behaviors, aggregated at the regional level, can improve forecasts of macro unemployment rates. These methods and results highlight promise of new data resources to measure micro economic behavior and improve estimates of critical economic indicators.

Connected through hyperedge HE-1 (induced by source paper "theory in theory out how social theory can solve problems that machine learning can t"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "measurement and fairness"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "theory in theory out the uses of social theory in machine learning for social science"):
  with members:
    - "50 years of test un fairness lessons for machine learning"
    - "towards a critical race methodology in algorithmic fairness"
    - "racial categories in machine learning"
    - "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"
    - "privacy ethics and data access a case study of the fragile families challenge"
    - "an intersectional definition of fairness"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "measuring economic activities of china with mobile big data"):
  with members:
    - "early warning of human crowds based on query data from baidu maps analysis based on shanghai stampede"
    - "store location selection via mining search query logs of baidu maps"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-4 (induced by source paper "estimating individual employment status using mobile phone network data"):
  with members:
    - "mobile communication signatures of unemployment"
    - "using deep learning to predict demographics from mobile phone metadata"
    - "evaluating socio economic state of a country analyzing airtime credit and mobile phone datasets"

Connected through hyperedge HE-5 (induced by source paper "mobile communication signatures of unemployment"):
  with members:
    - "an analytical framework to nowcast well being using mobile phone data"
    - "complex systems and a computational social science perspective on the labor market"

Connected through hyperedge HE-6 (induced by source paper "understanding predictability and exploration in human mobility"):
  with members:
    - "measuring large scale social networks with high resolution"

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)