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: "leveraging context to support automated food recognition in restaurants"
  Abstract: The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures reflecting what people eat. In this paper, we study how taking pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with additional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demonstrate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant's online menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food images taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).

Connected through hyperedge HE-1 (induced by source paper "foodx 251 a dataset for fine grained food classification"):
  with members:
    - "chinesefoodnet a large scale image dataset for chinese food recognition"
    - "combining weakly and webly supervised learning for classifying food images"
    - "webly supervised learning of convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-2 (induced by source paper "a survey of automatic methods for nutritional assessment"):
  with members:
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "efficient estimation of word representations in vector space"

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 "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 "toward storytelling from visual lifelogging an overview"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "f formation detection individuating free standing conversational groups in images"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"
    - "egocentric field of view localization using first person point of view devices"

Connected through hyperedge HE-6 (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"
    - "simultaneous food localization and recognition"
    - "webly supervised learning of convolutional networks"
    - "weakly supervised deep detection networks"
    - "learning visual features from large weakly supervised data"
    - "learning deep features for discriminative localization"
    - "training convolutional networks with noisy labels"

Connected through hyperedge HE-7 (induced by source paper "foodnet recognizing foods using ensemble of deep networks"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "simultaneous food localization and recognition"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "wide slice residual networks for food recognition"):
  with members:
    - "identity mappings in deep residual networks"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "convolutional two stream network fusion for video action recognition"
    - "wide residual networks"
    - "visual7w grounded question answering in images"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fine grained image classification by exploring bipartite graph labels"

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)