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: "from cookies to cooks insights on dietary patterns via analysis of web usage logs"
  Abstract: Nutrition is a key factor in people's overall health. Hence, understanding the nature and dynamics of population-wide dietary preferences over time and space can be valuable in public health. To date, studies have leveraged small samples of participants via food intake logs or treatment data. We propose a complementary source of population data on nutrition obtained via Web logs. Our main contribution is a spatiotemporal analysis of population-wide dietary preferences through the lens of logs gathered by a widely distributed Web-browser add-on, using the access volume of recipes that users seek via search as a proxy for actual food consumption. We discover that variation in dietary preferences as expressed via recipe access has two main periodic components, one yearly and the other weekly, and that there exist characteristic regional differences in terms of diet within the United States. In a second study, we identify users who show evidence of having made an acute decision to lose weight. We characterize the shifts in interests that they express in their search queries and focus on changes in their recipe queries in particular. Last, we correlate nutritional time series obtained from recipe queries with time-aligned data on hospital admissions, aimed at understanding how behavioral data captured in Web logs might be harnessed to identify potential relationships between diet and acute health problems. In this preliminary study, we focus on patterns of sodium identified in recipes over time and patterns of admission for congestive heart failure, a chronic illness that can be exacerbated by increases in sodium intake.

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"
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-2 (induced by source paper "a survey on food computing"):
  with members:
    - "food recommendation framework existing solutions and challenges"
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "recognition of multiple food items in a single photo for use in a buffet style restaurant"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "rice classification using spatio spectral deep convolutional neural network"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "single view food portion estimation learning image to energy mappings using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "predicting antimicrobial drug consumption using web search data"):
  with members:
    - "seasonal web search query selection for influenza like illness ili estimation"
    - "a study of metrics of distance and correlation between ranked lists for compositionality detection"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "ensemble learned vaccination uptake prediction using web search queries"
    - "findzebra a search engine for rare diseases"

Connected through hyperedge HE-4 (induced by source paper "harnessing the web for population scale physiological sensing a case study of sleep and performance"):
  with members:
    - "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "online actions with offline impact how online social networks influence online and offline user behavior"

Connected through hyperedge HE-5 (induced by source paper "food recommender systems important contributions challenges and future research directions"):
  with members:
    - "you tweet what you eat studying food consumption through twitter"
    - "food recommendation using ontology and heuristics"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-6 (induced by source paper "kissing cuisines exploring worldwide culinary habits on the web"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "foodporn obesity patterns in culinary interactions"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "you tweet what you eat studying food consumption through twitter"
    - "api design for machine learning software experiences from the scikit learn project"
    - "adadelta an adaptive learning rate method"
    - "a unified approach to mapping and clustering of bibliometric networks"
    - "modularity clustering is force directed layout"

Connected through hyperedge HE-7 (induced by source paper "fetishizing food in digital age foodporn around the world"):
  with members:
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-8 (induced by source paper "insights from machine learned diet success prediction"):
  with members:
    - "persistent sharing of fitness app status on twitter"
    - "you tweet what you eat studying food consumption through twitter"
    - "recipe recommendation using ingredient networks"

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)