human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spatial morphing kernel regression for feature interpolation"):
  Abstract: In recent years, geotagged social media has become popular as a novel source for geographic knowledge discovery. Ground-level images and videos provide a different perspective than overhead imagery and can be applied to a range of applications such as land use mapping, activity detection, pollution mapping, etc. The sparse and uneven distribution of this data presents a problem, however, for generating dense maps. We therefore investigate the problem of spatially interpolating the high-dimensional features extracted from sparse social media to enable dense labeling using standard classifiers. Further, we show how prior knowledge about region boundaries can be used to improve the interpolation through spatial morphing kernel regression. We show that an interpolate-then-classify framework can produce dense maps from sparse observations but that care must be taken in choosing the interpolation method. We also show that the spatial morphing kernel improves the results.

Includes members:
  Member "fine grained land use classification at the city scale using ground level images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution")
  Member "large scale mapping of human activity using geo tagged videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using conditional generative adversarial networks to generate ground level views from overhead imagery")
  Member "a unified model for near and remote sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideinfnet a deep neural network for semi automatic semantic segmentation with side information")
  Member "spatio temporal sentiment hotspot detection using geotagged photos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using conditional generative adversarial networks to generate ground level views from overhead imagery")
  Member "land use classification using convolutional neural networks applied to ground level images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding urban landuse from the above and ground perspectives a deep learning multimodal solution")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "using user generated online photos to estimate and monitor air pollution in major cities":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maplur exploring a new paradigm for estimating air pollution using deep learning on map images")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

Question: Please tell me which class the source paper 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)