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: "using user generated online photos to estimate and monitor air pollution in major cities"
  Abstract: With the rapid development of economy in China over the past decade, air pollution has become an increasingly serious problem in major cities and caused grave public health concerns in China. Recently, a number of studies have dealt with air quality and air pollution. Among them, some attempt to predict and monitor the air quality from different sources of information, ranging from deployed physical sensors to social media. These methods are either too expensive or unreliable, prompting us to search for a novel and effective way to sense the air quality. In this study, we propose to employ the state of the art in computer vision techniques to analyze photos that can be easily acquired from online social media. Next, we establish the correlation between the haze level computed directly from photos with the official PM 2.5 record of the taken city at the taken time. Our experiments based on both synthetic and real photos have shown the promise of this image-based approach to estimating and monitoring air pollution.

Connected through hyperedge HE-1 (induced by source paper "maplur exploring a new paradigm for estimating air pollution using deep learning on map images"):
  with members:
    - "rewarding coreference resolvers for being consistent with world knowledge"
    - "exploring the limits of weakly supervised pretraining"
    - "fine tuned language models for text classification"
    - "self normalizing neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "empirical evaluation of rectified activations in convolutional network"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-2 (induced by source paper "using conditional generative adversarial networks to generate ground level views from overhead imagery"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "spatial morphing kernel regression for feature interpolation"
    - "large scale mapping of human activity using geo tagged videos"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "spatial morphing kernel regression for feature interpolation"
    - "cross view image synthesis using geometry guided conditional gans"
    - "large scale mapping of human activity using geo tagged videos"
    - "began boundary equilibrium generative adversarial networks"
    - "pose guided person image generation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "semantic image synthesis via adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "spatial morphing kernel regression for feature interpolation"):
  with members:
    - "fine grained land use classification at the city scale using ground level images"
    - "large scale mapping of human activity using geo tagged videos"
    - "a unified model for near and remote sensing"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "land use classification using convolutional neural networks applied to ground level images"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "benchmarking single image dehazing and beyond"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "semantic foggy scene understanding with synthetic data"
    - "end to end united video dehazing and detection"
    - "sparse to dense depth prediction from sparse depth samples and a single image"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "a unified model for near and remote sensing"):
  with members:
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "understanding and mapping natural beauty"
    - "deep learning the city quantifying urban perception at a global scale"
    - "torontocity seeing the world with a million eyes"
    - "land use classification using convolutional neural networks applied to ground level images"
    - "predicting ground level scene layout from aerial imagery"
    - "snowwatch snow monitoring through acquisition and analysis of user generated content"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-7 (induced by source paper "using social media to predict the future a systematic literature review"):
  with members:
    - "what the language you tweet says about your occupation"
    - "mining online social data for detecting social network mental disorders"
    - "sentiment aggregate functions for political opinion polling using microblog streams"
    - "beating the news with embers forecasting civil unrest using open source indicators"
    - "predicting crowd behavior with big public data"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"

Connected through hyperedge HE-8 (induced by source paper "image dehazing using bilinear composition loss function"):
  with members:
    - "dehazenet an end to end system for single image haze removal"

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)