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: "estimating food consumption and poverty indices with mobile phone data"
  Abstract: Recent studies have shown the value of mobile phone data to tackle problems related to economic development and humanitarian action. In this research, we assess the suitability of indicators derived from mobile phone data as a proxy for food security indicators. We compare the measures extracted from call detail records and airtime credit purchases to the results of a nationwide household survey conducted at the same time. Results show high correlations (> .8) between mobile phone data derived indicators and several relevant food security variables such as expenditure on food or vegetable consumption. This correspondence suggests that, in the future, proxies derived from mobile phone data could be used to provide valuable up-to-date operational information on food security throughout low and middle income countries.

Connected through hyperedge HE-1 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-2 (induced by source paper "an analytical framework to nowcast well being using mobile phone data"):
  with members:
    - "harnessing mobile phone social network topology to infer users demographic attributes"
    - "evaluating socio economic state of a country analyzing airtime credit and mobile phone datasets"
    - "a planetary nervous system for social mining and collective awareness"
    - "how to create an innovation accelerator"
    - "four degrees of separation"

Connected through hyperedge HE-3 (induced by source paper "can mobile usage predict illiteracy in a developing country"):
  with members:
    - "evaluating socio economic state of a country analyzing airtime credit and mobile phone datasets"

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)