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: "wide slice residual networks for food recognition"
  Abstract: Food diary applications represent a tantalizing market. Such applications, based on image food recognition, opened to new challenges for computer vision and pattern recognition algorithms. Recent works in the field are focusing either on hand-crafted representations or on learning these by exploiting deep neural networks. Despite the success of such a last family of works, these generally exploit off-the shelf deep architectures to classify food dishes. Thus, the architectures are not cast to the specific problem. We believe that better results can be obtained if the deep architecture is defined with respect to an analysis of the food composition. Following such an intuition, this work introduces a new deep scheme that is designed to handle the food structure. Specifically, inspired by the recent success of residual deep network, we exploit such a learning scheme and introduce a slice convolution block to capture the vertical food layers. Outputs of the deep residual blocks are combined with the sliced convolution to produce the classification score for specific food categories. To evaluate our proposed architecture we have conducted experimental results on three benchmark datasets. Results demonstrate that our solution shows better performance with respect to existing approaches (e.g., a top-1 accuracy of 90.27% on the Food-101 challenging dataset).

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"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "food recognition using fusion of classifiers based on cnns"

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"
    - "grab pay and eat semantic food detection for smart restaurants"

Connected through hyperedge HE-3 (induced by source paper "dividing and conquering cross modal recipe retrieval from nearest neighbours baselines to sota"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "kitchenette predicting and ranking food ingredient pairings using siamese neural network"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "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"
    - "arcface additive angular margin loss for deep face recognition"
    - "stacked cross attention for image text matching"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "foodtracker a real time food detection mobile application by deep convolutional neural networks"):
  with members:
    - "a multi task learning approach for meal assessment"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "yolo9000 better faster stronger"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding data augmentation for classification when to warp"
    - "you only look once unified real time object detection"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "recognition of multiple food items in a single photo for use in a buffet style restaurant"):
  with members:
    - "significance of softmax based features in comparison to distance metric learning based features"

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.CV(Computer Vision and Pattern Recognition)