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: "spatio temporal sentiment hotspot detection using geotagged photos"
  Abstract: We perform spatio-temporal analysis of public sentiment using geotagged photo collections. We develop a deep learning-based classifier that predicts the emotion conveyed by an image. This allows us to associate sentiment with place. We perform spatial hotspot detection and show that different emotions have distinct spatial distributions that match expectations. We also perform temporal analysis using the capture time of the photos. Our spatio-temporal hotspot detection correctly identifies emerging concentrations of specific emotions and year-by-year analyses of select locations show there are strong temporal correlations between the predicted emotions and known events.

Connected through hyperedge HE-1 (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"
    - "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"
    - "using user generated online photos to estimate and monitor air pollution in major cities"

Connected through hyperedge HE-2 (induced by source paper "detecting permanent and intermittent purchase hotspots via computational stigmergy"):
  with members:
    - "a stigmergy based analysis of city hotspots to discover trends and anomalies in urban transportation usage"
    - "stigmergy based modeling to discover urban activity patterns from positioning data"

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 "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-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"
    - "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 "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"
    - "depth2action exploring embedded depth for large scale action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "youtube 8m a large scale video classification benchmark"

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)