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 "large scale and high resolution analysis of food purchases and health outcomes"):
  Abstract: To complement traditional dietary surveys, which are costly and of limited scale, researchers have resorted to digital data to infer the impact of eating habits on people’s health. However, online studies are limited in resolution: they are carried out at country or regional level and do not capture precisely the composition of the food consumed. We study the association between food consumption (derived from the loyalty cards of the main grocery retailer in London) and health outcomes (derived from publicly-available medical prescription records of all general practitioners in the city). The scale and granularity of our analysis is unprecedented: we analyze 1.6B food item purchases and 1.1B medical prescriptions for the entire city of London over the course of one year. By studying food consumption down to the level of nutrients, we show that nutrient diversity and amount of calories are the two strongest predictors of the prevalence of three diseases related to what is called the “metabolic syndrome”: hypertension, high cholesterol, and diabetes. This syndrome is a cluster of symptoms generally associated with obesity, is common across the rich world, and affects one in four adults in the UK. Our linear regression models achieve an $R^{2}$ of 0.6 when estimating the prevalence of diabetes in nearly 1000 census areas in London, and a classifier can identify (un)healthy areas with up to 91% accuracy. Interestingly, healthy areas are not necessarily well-off (income matters less than what one would expect) and have distinctive features: they tend to systematically eat less carbohydrates and sugar, diversify nutrients, and avoid large quantities. More generally, our study shows that analytics of digital records of grocery purchases can be used as a cheap and scalable tool for health surveillance and, upon these records, different stakeholders from governments to insurance companies to food companies could implement effective prevention strategies.

Includes members:
  Member "face to bmi using computer vision to infer body mass index on social media":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fooling computer vision into inferring the wrong body mass index")
  Member "is saki delicious the food perception gap on instagram and its relation to health":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction")
  Member "market basket prediction using user centric temporal annotated recurring sequences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a long short demands aware model for next item recommendation")
  Member "fetishizing food in digital age foodporn around the world":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "kissing cuisines exploring worldwide culinary habits on the web":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "foodai food image recognition via deep learning for smart food logging")
  Member "crowdsourcing health labels inferring body weight from profile pictures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "is saki delicious the food perception gap on instagram and its relation to health")
  Member "foodporn obesity patterns in culinary interactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "foodai food image recognition via deep learning for smart food logging")
  Member "you tweet what you eat studying food consumption through twitter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "foodai food image recognition via deep learning for smart food logging")

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.CY(Computers and Society)