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: "temporal convolutional neural network for the classification of satellite image time series"
  Abstract: New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth's surfaces. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS makes possible to monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not make the most of the temporal domain. Conversely, some approaches that take into account the temporal dimension have recently been tested, especially Recurrent Neural Networks (RNNs). This paper proposes an exhaustive study of another deep learning approaches, namely Temporal Convolutional Neural Networks (TempCNNs) where convolutions are applied in the temporal dimension. The goal is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification. This paper proposes a set of experiments performed on one million time series extracted from 46 Formosat-2 images. The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification. We also highlight some differences with results obtained in computer vision, e.g. about pooling layers. Moreover, we provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size. Finally, we assess the visual quality of the land cover maps produced by TempCNNs.

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"
    - "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"
    - "multi temporal land cover classification with sequential recurrent encoders"

Connected through hyperedge HE-2 (induced by source paper "inceptiontime finding alexnet for time series classification"):
  with members:
    - "convtimenet a pre trained deep convolutional neural network for time series classification"
    - "automatic alignment of surgical videos using kinematic data"
    - "deep neural network ensembles for time series classification"
    - "adversarial attacks on deep neural networks for time series classification"
    - "a cnn adapted to time series for the classification of supernovae"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "transfer learning for time series classification"
    - "the ucr time series archive"

Connected through hyperedge HE-3 (induced by source paper "ts chief a scalable and accurate forest algorithm for time series classification"):
  with members:
    - "scalable dictionary classifiers for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "from bop to boss and beyond time series classification with dictionary based classifiers"
    - "scalable and accurate deep learning for electronic health records"
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"

Connected through hyperedge HE-4 (induced by source paper "satellite image time series classification with pixel set encoders and temporal self attention"):
  with members:
    - "self attention for raw optical satellite time series classification"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "convolutional lstms for cloud robust segmentation of remote sensing imagery"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "convolutional two stream network fusion for video action recognition"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-5 (induced by source paper "object based multi temporal and multi source land cover mapping leveraging hierarchical class relationships"):
  with members:
    - "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"
    - "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-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"
    - "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"
    - "multi temporal land cover classification with sequential recurrent encoders"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "time series classification for varying length series"):
  with members:
    - "deep learning for time series classification a review"
    - "efficient discovery of variable length time series motifs with large length range in million scale time series"
    - "fast and accurate time series classification with weasel"
    - "time series classification from scratch with deep neural networks a strong baseline"

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)