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: "towards bottom up analysis of social food"
  Abstract: Social media provide a wealth of information for research into public health by providing a rich mix of personal data, location, hashtags, and social network information. Among these, Instagram has been recently the subject of many computational social science studies. However despite Instagram's focus on image sharing, most studies have exclusively focused on the hashtag and social network structure. In this paper we perform the first large scale content analysis of Instagram posts, addressing both the image and the associated hashtags, aiming to understand the content of partially-labelled images taken in-the-wild and the relationship with hashtags that individuals use as noisy labels. In particular, we explore the possibility of learning to recognise food image content in a data driven way, discovering both the categories of food, and how to recognise them, purely from social network data. Notably, we demonstrate that our approach to food recognition can often achieve accuracies greater than 70% in recognising popular food-related image categories, despite using no manual annotation. We highlight the current capabilities and future challenges and opportunities for such data-driven analysis of image content and the relation to hashtags.

Connected through hyperedge HE-1 (induced by source paper "scraping social media photos posted in kenya and elsewhere to detect and analyze food types"):
  with members:
    - "yolov3 an incremental improvement"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "fetishizing food in digital age foodporn around the world"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "wide slice residual networks for food recognition"

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"

Connected through hyperedge HE-3 (induced by source paper "a hybrid deep learning architecture for privacy preserving mobile analytics"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "federated learning strategies for improving communication efficiency"
    - "tapas tricks to accelerate encrypted prediction as a service"
    - "protecting sensory data against sensitive inferences"
    - "deep learning in mobile and wireless networking a survey"
    - "deepsecure scalable provably secure deep learning"
    - "inverting visual representations with convolutional networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"

Connected through hyperedge HE-4 (induced by source paper "is saki delicious the food perception gap on instagram and its relation to health"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "deepfood deep learning based food image recognition for computer aided dietary assessment"
    - "crowdsourcing health labels inferring body weight from profile pictures"
    - "deep image scaling up image recognition"
    - "deep residual learning for image recognition"
    - "social media image analysis for public health"
    - "foodporn obesity patterns in culinary interactions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving deep inference for rich user data on the cloud"):
  with members:
    - "deepsecure scalable provably secure deep learning"
    - "going deeper in facial expression recognition using deep neural networks"
    - "inverting visual representations with convolutional networks"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "privacy leakage in mobile computing tools methods and characteristics"
    - "caffe convolutional architecture for fast feature embedding"

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.CY(Computers and Society)