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: "crowdsourcing health labels inferring body weight from profile pictures"
  Abstract: To use social media for health-related analysis, one key step is the detection of health-related labels for users. But unlike transient conditions like flu, social media users are less vocal about chronic conditions such as obesity, as users might not tweet "I'm still overweight". As, however, obesity-related conditions such as diabetes, heart disease, osteoarthritis, and even cancer are on the rise, this obese-or-not label could be one of the most useful for studies in public health. #R##N#In this paper we investigate the feasibility of using profile pictures to infer if a user is overweight or not. We show that this is indeed possible and further show that the fraction of labeled-as-overweight users is higher in U.S. counties with higher obesity rates. Going from public to individual health analysis, we then find differences both in behavior and social networks, for example finding users labeled as overweight to have fewer followers.

Connected through hyperedge HE-1 (induced by source paper "large scale and high resolution analysis of food purchases and health outcomes"):
  with members:
    - "face to bmi using computer vision to infer body mass index on social media"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "market basket prediction using user centric temporal annotated recurring sequences"
    - "fetishizing food in digital age foodporn around the world"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-2 (induced by source paper "is saki delicious the food perception gap on instagram and its relation to health"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "towards bottom up analysis of social food"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"
    - "social media image analysis for public health"
    - "foodporn obesity patterns in culinary interactions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "halal culture on instagram"):
  with members:
    - "privacy and twitter in qatar traditional values in the digital world"
    - "failedrevolutions using twitter to study the antecedents of isis support"

Connected through hyperedge HE-4 (induced by source paper "quantified self meets social media sharing of weight updates on twitter"):
  with members:
    - "insights from machine learned diet success prediction"
    - "you tweet what you eat studying food consumption through twitter"

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.HC(Human-Computer Interaction)