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: "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.

Connected through hyperedge HE-1 (induced by source paper "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images"):
  with members:
    - "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
    - "predicting us state level agricultural sentiment as a measure of food security with tweets from farming communities"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "everybody dance now"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "density estimation using real nvp"

Connected through hyperedge HE-2 (induced by source paper "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"):
  with members:
    - "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images"
    - "predicting us state level agricultural sentiment as a measure of food security with tweets from farming communities"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "predicting us state level agricultural sentiment as a measure of food security with tweets from farming communities"):
  with members:
    - "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images"
    - "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
    - "convolutional neural networks for sentence classification"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

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)