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: "deep learning the city quantifying urban perception at a global scale"
  Abstract: Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city's physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.

Connected through hyperedge HE-1 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes"):
  with members:
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "disentangled person image generation"
    - "unsupervised cross domain image generation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-2 (induced by source paper "indexical cities articulating personal models of urban preference with geotagged data"):
  with members:
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
    - "very deep convolutional networks for large scale image recognition"
    - "computing with contextual numbers"

Connected through hyperedge HE-3 (induced by source paper "urban i from urban scenes to mapping slums transport modes and pedestrians in cities using deep learning and computer vision"):
  with members:
    - "midgar detection of people through computer vision in the internet of things scenarios to improve the security in smart cities smart towns and smart homes"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "foveanet perspective aware urban scene parsing"
    - "rethinking atrous convolution for semantic image segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "sodeep a sorting deep net to learn ranking loss surrogates"):
  with members:
    - "mediaeval 2018 predicting media memorability task"
    - "simple recurrent units for highly parallelizable recurrence"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "vse improving visual semantic embeddings with hard negatives"
    - "multimodal convolutional neural networks for matching image and sentence"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "automated detecting and placing road objects from street level images"):
  with members:
    - "yolov3 an incremental improvement"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "learning to navigate in cities without a map"
    - "automatic discovery and geotagging of objects from street view imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-6 (induced by source paper "streetscape augmentation using generative adversarial networks insights related to health and wellbeing"):
  with members:
    - "few shot unsupervised image to image translation"
    - "multimodal unsupervised image to image translation"
    - "high resolution deep convolutional generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "amortised map inference for image super resolution"

Connected through hyperedge HE-7 (induced by source paper "farsa fully automated roadway safety assessment"):
  with members:
    - "a unified model for near and remote sensing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding and mapping natural beauty"
    - "torontocity seeing the world with a million eyes"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-8 (induced by source paper "an unsupervised approach to geographical knowledge discovery using street level and street network images"):
  with members:
    - "modelling urban networks using variational autoencoders"
    - "augmenting correlation structures in spatial data using deep generative models"
    - "a pca like autoencoder"
    - "video to video synthesis"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "adam a method for stochastic optimization"

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)