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: "is saki delicious the food perception gap on instagram and its relation to health"
  Abstract: Food is an integral part of our life and what and how much we eat crucially affects our health. Our food choices largely depend on how we perceive certain characteristics of food, such as whether it is healthy, delicious or if it qualifies as a salad. But these perceptions differ from person to person and one person's "single lettuce leaf" might be another person's "side salad". Studying how food is perceived in relation to what it actually is typically involves a laboratory setup. Here we propose to use recent advances in image recognition to tackle this problem. Concretely, we use data for 1.9 million images from Instagram from the US to look at systematic differences in how a machine would objectively label an image compared to how a human subjectively does. We show that this difference, which we call the "perception gap", relates to a number of health outcomes observed at the county level. To the best of our knowledge, this is the first time that image recognition is being used to study the "misalignment" of how people describe food images vs. what they actually depict.

Connected through hyperedge HE-1 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"):
  with members:
    - "language in our time an empirical analysis of hashtags"
    - "hypergraph neural networks"
    - "simplicial closure and higher order link prediction"
    - "node2vec scalable feature learning for networks"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "learning hypergraph regularized attribute predictors"
    - "predicting multi actor collaborations using hypergraphs"

Connected through hyperedge HE-2 (induced by source paper "language in our time an empirical analysis of hashtags"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "hierarchical graph representation learning with differentiable pooling"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media"
    - "walk2friends inferring social links from mobility profiles"

Connected through hyperedge HE-3 (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"
    - "squeeze and excitation networks"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (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"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"

Connected through hyperedge HE-5 (induced by source paper "scraping social media photos posted in kenya and elsewhere to detect and analyze food types"):
  with members:
    - "yolov3 an incremental improvement"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fetishizing food in digital age foodporn around the world"
    - "towards bottom up analysis of social food"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "wide slice residual networks for food recognition"

Connected through hyperedge HE-6 (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"
    - "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"
    - "you tweet what you eat studying food consumption through twitter"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-7 (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"
    - "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"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-8 (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"

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)