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: "multi temporal land cover classification with sequential recurrent encoders"
  Abstract: Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of today’s sensors enable the use of temporal, along with spectral and spatial features.Domains such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results by using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells that reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, our experiments achieved state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing, compared to other classification approaches.

Connected through hyperedge HE-1 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"):
  with members:
    - "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"
    - "relation network for multi label aerial image classification"
    - "so2sat lcz42 a benchmark dataset for global local climate zones classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "mapping missing population in rural india a deep learning approach with satellite imagery"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"

Connected through hyperedge HE-2 (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"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "the ucr time series archive"

Connected through hyperedge HE-3 (induced by source paper "gating revisited deep multi layer rnns that can be trained"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "can recurrent neural networks warp time"
    - "the unreasonable effectiveness of the forget gate"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"

Connected through hyperedge HE-4 (induced by source paper "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "detecting hardly visible roads in low resolution satellite time series data"):
  with members:
    - "road extraction by deep residual u net"
    - "decoupled weight decay regularization"
    - "sgdr stochastic gradient descent with restarts"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (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"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "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-8 (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"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "land cover classification via multitemporal spatial data by deep recurrent neural networks"
    - "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"

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)