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: "exploring models and data for remote sensing image caption generation"
  Abstract: Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, notable progress has been made in scene classification and target detection. However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at   https://github.com/201528014227051/RSICD_optimal  .

Connected through hyperedge HE-1 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "xview objects in context in overhead imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "where to put the image in an image caption generator"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-3 (induced by source paper "scar spatial channel wise attention regression networks for crowd counting"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "weakly supervised adversarial domain adaptation for semantic segmentation in urban scenes"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "pcc net perspective crowd counting via spatial convolutional network"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "crowd counting using deep recurrent spatial aware network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"

Connected through hyperedge HE-4 (induced by source paper "hyperspectral image denoising employing a spatial spectral deep residual convolutional neural network"):
  with members:
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "denoising hyperspectral image with non i i d noise structure"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "sg fcn a motion and memory based deep learning model for video saliency detection"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "geometric hypergraph learning for visual tracking"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "adaptive compressive tracking via online vector boosting feature selection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "recent advances and opportunities in scene classification of aerial images with deep models"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "rsi cb a large scale remote sensing image classification benchmark via crowdsource data"
    - "deep residual learning for image recognition"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)