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: "simultaneous food localization and recognition"
  Abstract: The development of automatic nutrition diaries, which would allow to keep track objectively of everything we eat, could enable a whole new world of possibilities for people concerned about their nutrition patterns. With this purpose, in this paper we propose the first method for simultaneous food localization and recognition. Our method is based on two main steps, which consist in, first, produce a food activation map on the input image (i.e. heat map of probabilities) for generating bounding boxes proposals and, second, recognize each of the food types or food-related objects present in each bounding box. We demonstrate that our proposal, compared to the most similar problem nowadays - object localization, is able to obtain high precision and reasonable recall levels with only a few bounding boxes. Furthermore, we show that it is applicable to both conventional and egocentric images.

Connected through hyperedge HE-1 (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-2 (induced by source paper "cuisinenet food attributes classification using multi scale convolution network"):
  with members:
    - "food ingredients recognition through multi label learning"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "diversified visual attention networks for fine grained object classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "subset feature learning for fine grained category classification"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "grab pay and eat semantic food detection for smart restaurants"):
  with members:
    - "food recognition using fusion of classifiers based on cnns"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "analyzing first person stories based on socializing eating and sedentary patterns"):
  with members:
    - "recognizing activities of daily living from egocentric images"
    - "sentiment recognition in egocentric photostreams"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "tensorflow a system for large scale machine learning"
    - "the fast convergence of incremental pca"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "food ingredients recognition through multi label learning"):
  with members:
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "exploring food detection using cnns"
    - "wide slice residual networks for food recognition"
    - "cnn rnn a unified framework for multi label image classification"
    - "understanding neural networks through deep visualization"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "combining weakly and webly supervised learning for classifying food images"):
  with members:
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "webly supervised learning of convolutional networks"
    - "weakly supervised deep detection networks"
    - "leveraging context to support automated food recognition in restaurants"
    - "learning visual features from large weakly supervised data"
    - "learning deep features for discriminative localization"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-8 (induced by source paper "foodnet recognizing foods using ensemble of deep networks"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "leveraging context to support automated food recognition in restaurants"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

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)