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: "d4d senegal the second mobile phone data for development challenge"
  Abstract: The D4D-Senegal challenge is an open innovation data challenge on anonymous call patterns of Orange's mobile phone users in Senegal. The goal of the challenge is to help address society development questions in novel ways by contributing to the socio-economic development and well-being of the Senegalese population. Participants to the challenge are given access to three mobile phone datasets. This paper describes the three datasets. The datasets are based on Call Detail Records (CDR) of phone calls and text exchanges between more than 9 million of Orange's customers in Senegal between January 1, 2013 to December 31, 2013. The datasets are: (1) antenna-to-antenna traffic for 1666 antennas on an hourly basis, (2) fine-grained mobility data on a rolling 2-week basis for a year with bandicoot behavioral indicators at individual level for about 300,000 randomly sampled users, (3) one year of coarse-grained mobility data at arrondissement level with bandicoot behavioral indicators at individual level for about 150,000 randomly sampled users

Connected through hyperedge HE-1 (induced by source paper "better coverage better outcomes mapping mobile network data to official statistics using satellite imagery and radio propagation modelling"):
  with members:
    - "estimation of static and dynamic urban populations with mobile network metadata"
    - "performance and sensitivities of home detection from mobile phone data"
    - "can mobile usage predict illiteracy in a developing country"

Connected through hyperedge HE-2 (induced by source paper "optimizing city scale traffic flows through modeling isolated observations of vehicle movements"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "continuous time bayesian networks"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

Connected through hyperedge HE-3 (induced by source paper "assessing the quality of home detection from mobile phone data for official statistics"):
  with members:
    - "a comparison of spatial based targeted disease containment strategies using mobile phone data"
    - "an analytical framework to nowcast well being using mobile phone data"
    - "choosing the right home location definition method for the given dataset"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-4 (induced by source paper "mapping the privacy utility tradeoff in mobile phone data for development"):
  with members:
    - "on the anonymizability of mobile traffic datasets"
    - "privacy by design in big data an overview of privacy enhancing technologies in the era of big data analytics"

Connected through hyperedge HE-5 (induced by source paper "mobile phone metadata for development"):
  with members:
    - "evaluating socio economic state of a country analyzing airtime credit and mobile phone datasets"

Connected through hyperedge HE-6 (induced by source paper "data for refugees the d4r challenge on mobility of syrian refugees in turkey"):
  with members:
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-7 (induced by source paper "identifying seasonal mobility profiles from anonymized and aggregated mobile phone data application in food security"):
  with members:
    - "flooding through the lens of mobile phone activity"

Connected through hyperedge HE-8 (induced by source paper "variational inference with agent based models"):
  with members:
    - "virtual networks and poverty analysis in senegal"
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation propagation for approximate bayesian inference"
    - "graph coupled hmms for modeling the spread of infection"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"

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)