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: "a survey on food computing"
  Abstract: Food is very essential for human life and it is fundamental to the human experience. Food-related study may support multifarious applications and services, such as guiding the human behavior, improving the human health and understanding the culinary culture. With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries, leading to large-scale food data. Large-scale food data offers rich knowledge about food and can help tackle many central issues of human society. Therefore, it is time to group several disparate issues related to food computing. Food computing acquires and analyzes heterogenous food data from disparate sources for perception, recognition, retrieval, recommendation, and monitoring of food. In food computing, computational approaches are applied to address food related issues in medicine, biology, gastronomy and agronomy. Both large-scale food data and recent breakthroughs in computer science are transforming the way we analyze food data. Therefore, vast amounts of work has been conducted in the food area, targeting different food-oriented tasks and applications. However, there are very few systematic reviews, which shape this area well and provide a comprehensive and in-depth summary of current efforts or detail open problems in this area. In this paper, we formalize food computing and present such a comprehensive overview of various emerging concepts, methods, and tasks. We summarize key challenges and future directions ahead for food computing. This is the first comprehensive survey that targets the study of computing technology for the food area and also offers a collection of research studies and technologies to benefit researchers and practitioners working in different food-related fields.

Connected through hyperedge HE-1 (induced by source paper "cookgan meal image synthesis from ingredients"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "gilt generating images from long text"
    - "how to make a pizza learning a compositional layer based gan model"
    - "cgans with projection discriminator"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning cross modal embeddings with adversarial networks for cooking recipes and food images"):
  with members:
    - "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"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"

Connected through hyperedge HE-4 (induced by source paper "the art of food meal image synthesis from ingredients"):
  with members:
    - "cgans with projection discriminator"
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "progressive growing of gans for improved quality stability and variation"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "food recommendation framework existing solutions and challenges"):
  with members:
    - "hierarchical attention network for visually aware food recommendation"
    - "cross modal health state estimation"
    - "personalized classifier for food image recognition"
    - "food recommender systems important contributions challenges and future research directions"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-6 (induced by source paper "learning eating environments through scene clustering"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

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)