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: "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
  Abstract: Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly in the developing world. Not surprisingly, the poverty stricken regions are also the ones which have a high probability of being a war zone, have poor infrastructure and sometimes have governments that do not cooperate with internationally funded development efforts. We train a CNN on free and publicly available daytime satellite images of the African continent from Landsat 7 to build a model for predicting local economic livelihoods. Only 5% of the satellite images can be associated with labels (which are obtained from DHS Surveys) and thus a semi-supervised approach using a GAN (similar to the approach of Salimans, et al. (2016)), albeit with a more stable-to-train flavor of GANs called the Wasserstein GAN regularized with gradient penalty(Gulrajani, et al. (2017)) is used. The method of multitask learning is employed to regularize the network and also create an end-to-end model for the prediction of multiple poverty metrics.

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:
    - "predicting food security outcomes using convolutional neural networks cnns for satellite tasking"
    - "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 "dc al gan pseudoprogression and true tumor progression of glioblastoma multiform image classification based on dcgan and alexnet"):
  with members:
    - "generative adversarial networks an overview"
    - "generative adversarial networks and probabilistic graph models for hyperspectral image classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "a survey on deep learning in medical image analysis"
    - "synthetic medical images from dual generative adversarial networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "classification of alzheimer s disease using fmri data and deep learning convolutional neural networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "predicting food security outcomes using convolutional neural networks cnns for satellite tasking"):
  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"
    - "deepsat a learning framework for satellite imagery"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (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"
    - "predicting food security outcomes using convolutional neural networks cnns for satellite tasking"
    - "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)