human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "food recommendation framework existing solutions and challenges":
    also connected through other hyperedges:
      (none)
  Member "learning cross modal embeddings with adversarial networks for cooking recipes and food images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cookgan meal image synthesis from ingredients")
  Member "recognition of multiple food items in a single photo for use in a buffet style restaurant":
    also connected through other hyperedges:
      (none)
  Member "recipeqa a challenge dataset for multimodal comprehension of cooking recipes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "manymodalqa modality disambiguation and qa over diverse inputs")
  Member "scaling egocentric vision the epic kitchens dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to learn words from visual scenes")
  Member "rice classification using spatio spectral deep convolutional neural network":
    also connected through other hyperedges:
      (none)
  Member "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "foodai food image recognition via deep learning for smart food logging")
  Member "single view food portion estimation learning image to energy mappings using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep cooking predicting relative food ingredient amounts from images")

Question: Please tell me which class the source paper 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)