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: "food recognition using fusion of classifiers based on cnns"
  Abstract: With the arrival of Convolutional Neural Networks, the complex problem of food recognition has experienced an important improvement recently. The best results have been obtained using methods based on very deep Convolutional Neural Networks, which show that the deeper the model, the better the classification accuracy is. However, very deep neural networks may suffer from the overfitting problem. In this paper, we propose a combination of multiple classifiers based on Convolutional models that complement each other and thus, achieve an improvement in performance. The evaluation of our approach is done on 2 public datasets: Food-101 as a dataset with a wide variety of fine-grained dishes, and Food-11 as a dataset of high-level food categories, where our approach outperforms the independent Convolutional Neural Networks models.

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"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "a multi task learning approach for meal assessment"):
  with members:
    - "grab pay and eat semantic food detection for smart restaurants"
    - "unsupervised learning of depth and ego motion from video"
    - "two view 3d reconstruction for food volume estimation"
    - "demon depth and motion network for learning monocular stereo"
    - "mask r cnn"
    - "wide slice residual networks for food recognition"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "grab pay and eat semantic food detection for smart restaurants"):
  with members:
    - "food ingredients recognition through multi label learning"
    - "exploring food detection using cnns"
    - "yolo9000 better faster stronger"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "wide slice residual networks for food recognition"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"

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)