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: "land use classification using convolutional neural networks applied to ground level images"
  Abstract: Land use mapping is a fundamental yet challenging task in geographic science. In contrast to land cover mapping, it is generally not possible using overhead imagery. The recent, explosive growth of online geo-referenced photo collections suggests an alternate approach to geographic knowledge discovery. In this work, we present a general framework that uses ground-level images from Flickr for land use mapping. Our approach benefits from several novel aspects. First, we address the nosiness of the online photo collections, such as imprecise geolocation and uneven spatial distribution, by performing location and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our indoor/outdoor classifier achieves state-of-the-art performance on several bench- mark datasets and approaches human-level accuracy. Second, we utilize high-level semantic image features extracted using deep learning, specifically convolutional neural net- works, which allow us to achieve upwards of 76% accuracy on a challenging eight class land use mapping problem.

Connected through hyperedge HE-1 (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"
    - "a unified model for near and remote sensing"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "webvision challenge visual learning and understanding with web data"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "adaptive deep pyramid matching for remote sensing scene classification"

Connected through hyperedge HE-4 (induced by source paper "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "remote sensing image scene classification benchmark and state of the art"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "visual descriptors for content based retrieval of remote sensing images"

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"
    - "a unified model for near and remote sensing"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "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 "large scale mapping of human activity using geo tagged videos"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "deep local video feature for action recognition"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "guided optical flow learning"
    - "spatio temporal sentiment hotspot detection using geotagged photos"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-7 (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"
    - "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"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "spatio temporal sentiment hotspot detection using geotagged photos"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "urban magnetism through the lens of geo tagged photography"

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)