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: "a unified model for near and remote sensing"
  Abstract: We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead and ground-level images in an end-to-end trainable neural network, which uses kernel regression and density estimation to convert features extracted from the ground-level images into a dense feature map. The output of this network is a dense estimate of the geospatial function in the form of a pixel-level labeling of the overhead image. To evaluate our approach, we created a large dataset of overhead and ground-level images from a major urban area with three sets of labels: land use, building function, and building age. We find that our approach is more accurate for all tasks, in some cases dramatically so.

Connected through hyperedge HE-1 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information"):
  with members:
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep grabcut for object selection"
    - "convolutional networks with adaptive inference graphs"
    - "pose2instance harnessing keypoints for person instance segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution"):
  with members:
    - "deep multi task learning for a geographically regularized semantic segmentation of aerial images"
    - "fine grained land use classification at the city scale using ground level images"
    - "building instance classification using street view images"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"

Connected through hyperedge HE-3 (induced by source paper "farsa fully automated roadway safety assessment"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding and mapping natural beauty"
    - "deep learning the city quantifying urban perception at a global scale"
    - "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-4 (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-5 (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"
    - "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"
    - "using user generated online photos to estimate and monitor air pollution in major cities"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "understanding and mapping natural beauty"):
  with members:
    - "planet photo geolocation with convolutional neural networks"
    - "horizon lines in the wild"
    - "predicting ground level scene layout from aerial imagery"
    - "understanding image virality"
    - "wide area image geolocalization with aerial reference imagery"
    - "we are humor beings understanding and predicting visual humor"
    - "the digital life of walkable streets"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

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)