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 "predicting food security outcomes using convolutional neural networks cnns for satellite tasking"):
  Abstract: Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world. We train a CNN on publicly available satellite data describing land cover classification and use both transfer learning and direct training to build a model for FSM prediction purely from satellite imagery data. We then propose efficient tasking algorithms for high resolution satellite assets via transfer learning, Markovian search algorithms, and Bayesian networks.

Includes members:
  Member "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring multiscale map styles using generative adversarial networks")
  Member "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images")
  Member "predicting us state level agricultural sentiment as a measure of food security with tweets from farming communities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images")
  Member "deepsat a learning framework for satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")

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)