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: "land cover classification via multitemporal spatial data by deep recurrent neural networks"
  Abstract: Nowadays, modern earth observation programs produce huge volumes of satellite images time series that can be useful to monitor geographical areas through time. How to efficiently analyze such a kind of information is still an open question in the remote sensing field. Recently, deep learning methods proved suitable to deal with remote sensing data mainly for scene classification(i.e., convolutional neural networks on single images) while only very few studies exist involving temporal deep learning approaches [i.e., recurrent neural networks (RNNs)] to deal with remote sensing time series. In this letter, we evaluate the ability of RNNs, in particular, the long short-term memory (LSTM) model, to perform land cover classification considering multitemporal spatial data derived from a time series of satellite images. We carried out experiments on two different data sets considering both pixel-based and object-based classifications. The obtained results show that RNNs are competitive compared with the state-of-the-art classifiers, and may outperform classical approaches in the presence of low represented and/or highly mixed classes. We also show that the alternative feature representation generated by LSTM can improve the performances of standard classifiers.

Connected through hyperedge HE-1 (induced by source paper "object based multi temporal and multi source land cover mapping leveraging hierarchical class relationships"):
  with members:
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "fine grained attention mechanism for neural machine translation"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "attention is all you need"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "efficient attention using a fixed size memory representation"
    - "why should i trust you explaining the predictions of any classifier"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "supervised level wise pretraining for recurrent neural network initialization in multi class classification"):
  with members:
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "efficient attention using a fixed size memory representation"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "a framework for remote sensing images processing using deep learning techniques"):
  with members:
    - "deep learning in remote sensing a review"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "a survey on object detection in optical remote sensing images"
    - "a generic framework for the development of geospatial processing pipelines on clusters"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "semi supervised learning with graphs covariance based superpixels for hyperspectral image classification"):
  with members:
    - "peekaboo where are the objects structure adjusting superpixels"

Connected through hyperedge HE-5 (induced by source paper "temporal convolutional neural network for the classification of satellite image time series"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "deep learning for time series classification a review"
    - "missing data reconstruction in remote sensing image with a unified spatial temporal spectral deep convolutional neural network"
    - "3 d deep learning approach for remote sensing image classification"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-6 (induced by source paper "duplo a dual view point deep learning architecture for time series classification"):
  with members:
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "mrfusion a deep learning architecture to fuse pan and ms imagery for land cover mapping"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "efficient attention using a fixed size memory representation"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-7 (induced by source paper "target adaptive cnn based pansharpening"):
  with members:
    - "deep learning in remote sensing a review"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a survey of pansharpening methods with a new band decoupled variational model"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-8 (induced by source paper "deep learning in agriculture a survey"):
  with members:
    - "deep recurrent neural networks for mapping winter vegetation quality coverage via multi temporal sar sentinel 1"
    - "an analysis of deep neural network models for practical applications"
    - "crop planning using stochastic visual optimization"
    - "using deep learning for image based plant disease detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep learning in neural networks"
    - "deep plant plant identification with convolutional neural networks"
    - "going deeper with convolutions"

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)