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: "computer vision based food calorie estimation dataset method and experiment"
  Abstract: Computer vision has been introduced to estimate calories from food images. But current food image data sets don't contain volume and mass records of foods, which leads to an incomplete calorie estimation. In this paper, we present a novel food image data set with volume and mass records of foods, and a deep learning method for food detection, to make a complete calorie estimation. Our data set includes 2978 images, and every image contains corresponding each food's annotation, volume and mass records, as well as a certain calibration reference. To estimate calorie of food in the proposed data set, a deep learning method using Faster R-CNN first is put forward to detect the food. And the experiment results show our method is effective to estimate calories and our data set contains adequate information for calorie estimation. Our data set is the first released food image data set which can be used to evaluate computer vision-based calorie estimation methods.

Connected through hyperedge HE-1 (induced by source paper "deep cooking predicting relative food ingredient amounts from images"):
  with members:
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "single view food portion estimation learning image to energy mappings using generative adversarial networks"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "inverse cooking recipe generation from food images"
    - "chinesefoodnet a large scale image dataset for chinese food recognition"
    - "two view 3d reconstruction for food volume estimation"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

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"

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)