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: "personalized classifier for food image recognition"
  Abstract: Currently, food image recognition tasks are evaluated against fixed datasets. However, in real-world conditions, there are cases in which the number of samples in each class continues to increase and samples from novel classes appear. In particular, dynamic datasets in which each individual user creates samples and continues the updating process often have content that varies considerably between different users, and the number of samples per person is very limited. A single classifier common to all users cannot handle such dynamic data. Bridging the gap between the laboratory environment and the real world has not yet been accomplished on a large scale. Personalizing a classifier incrementally for each user is a promising way to do this. In this paper, we address the personalization problem, which involves adapting to the user's domain incrementally using a very limited number of samples. We propose a simple yet effective personalization framework which is a combination of the nearest class mean classifier and the 1-nearest neighbor classifier based on deep features. To conduct realistic experiments, we made use of a new dataset of daily food images collected by a food-logging application. Experimental results show that our proposed method significantly outperforms existing methods.

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"
    - "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"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "food recommendation framework existing solutions and challenges"):
  with members:
    - "a survey on food computing"
    - "hierarchical attention network for visually aware food recommendation"
    - "cross modal health state estimation"
    - "food recommender systems important contributions challenges and future research directions"
    - "recipe recommendation using ingredient networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "inverse cooking recipe generation from food images"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "hierarchical neural story generation"
    - "exploring the limits of weakly supervised pretraining"
    - "attention is all you need"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "learning deep latent spaces for multi label classification"

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)