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 us state level agricultural sentiment as a measure of food security with tweets from farming communities"
  Abstract: The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food aid programs. As a result, a great deal of work has been dedicated to predicting important food security metrics such as annual crop yields using a variety of methods including simulation, remote sensing, weather models, and human expert input. As a complement to existing techniques in crop yield prediction, this work develops neural network models for predicting the sentiment of Twitter feeds from farming communities. Specifically, we investigate the potential of both direct learning on a small dataset of agriculturally-relevant tweets and transfer learning from larger, well-labeled sentiment datasets from other domains (e.g.~politics) to accurately predict agricultural sentiment, which we hope would ultimately serve as a useful crop yield predictor. We find that direct learning from small, relevant datasets outperforms transfer learning from large, fully-labeled datasets, that convolutional neural networks broadly outperform recurrent neural networks on Twitter sentiment classification, and that these models perform substantially less well on ternary sentiment problems characteristic of practical settings than on binary problems often found in the literature.

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"
    - "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
    - "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 food security outcomes using convolutional neural networks cnns for satellite tasking"
    - "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 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"
    - "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty"
    - "deepsat a learning framework for satellite imagery"
    - "very deep convolutional networks for large scale image recognition"

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.CL(Computation and Language)