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: "kissing cuisines exploring worldwide culinary habits on the web"
  Abstract: Food and nutrition occupy an increasingly prevalent space on the web, and dishes and recipes shared online provide an invaluable mirror into culinary cultures and attitudes around the world. More specifically, ingredients, flavors, and nutrition information become strong signals of the taste preferences of individuals and civilizations. However, there is little understanding of these palate varieties. In this paper, we present a large-scale study of recipes published on the web and their content, aiming to understand cuisines and culinary habits around the world. Using a database of more than 157K recipes from over 200 different cuisines, we analyze ingredients, flavors, and nutritional values which distinguish dishes from different regions, and use this knowledge to assess the predictability of recipes from different cuisines. We then use country health statistics to understand the relation between these factors and health indicators of different nations, such as obesity, diabetes, migration, and health expenditure. Our results confirm the strong effects of geographical and cultural similarities on recipes, health indicators, and culinary preferences across the globe.

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"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning cross modal embeddings with adversarial networks for cooking recipes and food images"):
  with members:
    - "a survey on food computing"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "can a chatbot determine my diet addressing challenges of chatbot application for meal recommendation"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "is saki delicious the food perception gap on instagram and its relation to health"

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"
    - "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"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-4 (induced by source paper "eating healthier exploring nutrition information for healthier recipe recommendation"):
  with members:
    - "mpe a mobility pattern embedding model for predicting next locations"
    - "nlpmm a next location predictor with markov modeling"
    - "distributed representations of words and phrases and their compositionality"
    - "a fast and simple algorithm for training neural probabilistic language models"
    - "recipe recommendation using ingredient networks"

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 "of wines and reviews measuring and modeling the vivino wine social network"):
  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"

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)