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: "you tweet what you eat studying food consumption through twitter"
  Abstract: Food is an integral part of our lives, cultures, and well-being, and is of major interest to public health. The collection of daily nutritional data involves keeping detailed diaries or periodic surveys and is limited in scope and reach. Alternatively, social media is infamous for allowing its users to update the world on the minutiae of their daily lives, including their eating habits. In this work we examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the state-wide obesity rates, achieving a Pearson correlation of 0.77 across the 50 US states and the District of Columbia. We then build a model to predict county-wide obesity and diabetes statistics based on a combination of demographic variables and food names mentioned on Twitter. Our results show significant improvement over previous CHI research (Culotta'14). We further link this data to societal and economic factors, such as education and income, illustrating that, for example, areas with higher education levels tweet about food that is significantly less caloric. Finally, we address the somewhat controversial issue of the social nature of obesity (first raised by Christakis & Fowler in 2007) by inducing two social networks using mentions and reciprocal following relationships.

Connected through hyperedge HE-1 (induced by source paper "foodai food image recognition via deep learning for smart food logging"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "squeeze and excitation networks"
    - "kissing cuisines exploring worldwide culinary habits on the web"

Connected through hyperedge HE-2 (induced by source paper "characterizing and predicting repeat food consumption behavior for just in time interventions"):
  with members:
    - "goal setting and achievement in activity tracking apps a case study of myfitnesspal"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "food recommender systems important contributions challenges and future research directions"
    - "fetishizing food in digital age foodporn around the world"
    - "extracting food substitutes from food diary via distributional similarity"
    - "insights from machine learned diet success prediction"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-3 (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"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-4 (induced by source paper "tracing state level obesity prevalence from sentence embeddings of tweets a feasibility study"):
  with members:
    - "starspace embed all the things"
    - "probabilistic inference of twitter users age based on what they follow"

Connected through hyperedge HE-5 (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-6 (induced by source paper "fake cures user centric modeling of health misinformation in social media"):
  with members:
    - "the spread of low credibility content by social bots"
    - "using facebook ads audiences for global lifestyle disease surveillance promises and limitations"
    - "the spread of fake news by social bots"
    - "halal culture on instagram"
    - "catching zika fever application of crowdsourcing and machine learning for tracking health misinformation on twitter"
    - "fake news detection on social media a data mining perspective"
    - "fetishizing food in digital age foodporn around the world"
    - "a cross platform collection of social network profiles"

Connected through hyperedge HE-7 (induced by source paper "of wines and reviews measuring and modeling the vivino wine social network"):
  with members:
    - "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 are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"

Connected through hyperedge HE-8 (induced by source paper "characterizing transgender health issues in twitter"):
  with members:
    - "characterizing diseases and disorders in gay users tweets"
    - "mining public opinion about economic issues twitter and the u s presidential election"
    - "computational content analysis of negative tweets for obesity diet diabetes and exercise"

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)