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: "grab pay and eat semantic food detection for smart restaurants"
  Abstract: The increase in awareness of people towards their nutritional habits has drawn considerable attention to the field of automatic food analysis. Focusing on self-service restaurants environment, automatic food analysis is not only useful for extracting nutritional information from foods selected by customers, it is also of high interest to speed up the service solving the bottleneck produced at the cashiers in times of high demand. In this paper, we address the problem of automatic food tray analysis in canteens and restaurants environment, which consists in predicting multiple foods placed on a tray image. We propose a new approach for food analysis based on convolutional neural networks, we name Semantic Food Detection, which integrates in the same framework food localization, recognition and segmentation. We demonstrate that our method improves the state of the art food detection by a considerable margin on the public dataset UNIMIB2016 achieving about 90% in terms of F-measure, and thus provides a significant technological advance towards the automatic billing in restaurant environments.

Connected through hyperedge HE-1 (induced by source paper "an artificial intelligence based system to assess nutrient intake for hospitalised patients"):
  with members:
    - "an artificial intelligence based system for nutrient intake assessment of hospitalised patients"
    - "foodx 251 a dataset for fine grained food classification"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "a survey on deep transfer learning"
    - "personalized classifier for food image recognition"
    - "a multi task learning approach for meal assessment"
    - "food recognition using fusion of classifiers based on cnns"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (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"
    - "a survey on food computing"
    - "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"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "on the effectiveness of convolutional autoencoders on image based personalized recommender systems"):
  with members:
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "the art of food meal image synthesis from ingredients"):
  with members:
    - "cgans with projection discriminator"
    - "a survey on food computing"
    - "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"
    - "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 "self attention and ingredient attention based model for recipe retrieval from image queries"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "a multi task learning approach for meal assessment"
    - "attention is all you need"
    - "two view 3d reconstruction for food volume estimation"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "fully automatic semantic segmentation for food intake tracking in long term care homes"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "identity mappings in deep residual networks"
    - "pyramid scene parsing network"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "hierarchical attention network for visually aware food recommendation"):
  with members:
    - "discrete factorization machines for fast feature based recommendation"
    - "adversarial personalized ranking for recommendation"
    - "aesthetic based clothing recommendation"
    - "neural collaborative filtering"
    - "item silk road recommending items from information domains to social users"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide slice residual networks for food 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.CV(Computer Vision and Pattern Recognition)