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: "duplo a dual view point deep learning architecture for time series classification"
  Abstract: Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data. #R##N#Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely \method{} (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the \textit{Gard} site in France and the \textit{Reunion Island} in the Indian Ocean), demonstrate the significance of our proposal.

Connected through hyperedge HE-1 (induced by source paper "self attention for raw optical satellite time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "massively parallel hyperparameter tuning"
    - "the uea multivariate time series classification archive 2018"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tune a research platform for distributed model selection and training"
    - "the ucr time series archive"
    - "multi temporal land cover classification with sequential recurrent encoders"

Connected through hyperedge HE-2 (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"
    - "attention is all you need"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "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-3 (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"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "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-4 (induced by source paper "early classification for agricultural monitoring from satellite time series"):
  with members:
    - "end to end learning for early classification of time series"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "land cover classification from multi temporal multi spectral remotely sensed imagery using patch based recurrent neural networks"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "convolutional lstms for cloud robust segmentation of remote sensing imagery"):
  with members:
    - "deep learning based cloud detection for medium and high resolution remote sensing images of different sensors"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"

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)