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: "two view 3d reconstruction for food volume estimation"
  Abstract: The increasing prevalence of diet-related chronic diseases coupled with the ineffectiveness of traditional diet management methods have resulted in a need for novel tools to accurately and automatically assess meals. Recently, computer vision based systems that use meal images to assess their content have been proposed. Food portion estimation is the most difficult task for individuals assessing their meals and it is also the least studied area. The present paper proposes a three-stage system to calculate portion sizes using two images of a dish acquired by mobile devices. The first stage consists in understanding the configuration of the different views, after which a dense 3D model is built from the two images; finally, this 3D model serves to extract the volume of the different items. The system was extensively tested on 77 real dishes of known volume, and achieved an average error of less than 10% in 5.5 seconds per dish. The proposed pipeline is computationally tractable and requires no user input, making it a viable option for fully automated dietary assessment.

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 "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"
    - "grab pay and eat semantic food detection for smart restaurants"
    - "attention is all you need"
    - "skip thought vectors"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "computer vision based food calorie estimation dataset method and experiment"
    - "deep residual learning for image recognition"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a multi task learning approach for meal assessment"):
  with members:
    - "grab pay and eat semantic food detection for smart restaurants"
    - "food recognition using fusion of classifiers based on cnns"
    - "unsupervised learning of depth and ego motion from video"
    - "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"

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)