human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"):
  Abstract: Change detection is one of the central problems in earth observation and was extensively investigated over recent decades. In this paper, we propose a novel recurrent convolutional neural network (ReCNN) architecture, which is trained to learn a joint spectral–spatial–temporal feature representation in a unified framework for change detection in multispectral images. To this end, we bring together a convolutional neural network and a recurrent neural network into one end-to-end network. The former is able to generate rich spectral-spatial feature representations, while the latter effectively analyzes temporal dependence in bitemporal images. In comparison with previous approaches to change detection, the proposed network architecture possesses three distinctive properties: 1) it is end-to-end trainable, in contrast to most existing methods whose components are separately trained or computed; 2) it naturally harnesses spatial information that has been proven to be beneficial to change detection task; and 3) it is capable of adaptively learning the temporal dependence between multitemporal images, unlike most of the algorithms that use fairly simple operation like image differencing or stacking. As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis. The proposed network is validated on real multispectral data sets. Both visual and quantitative analyses of the experimental results demonstrate competitive performance in the proposed mode.

Includes members:
  Member "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  Member "multi temporal land cover classification with sequential recurrent encoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks")
  Member "large kernel matters improve semantic segmentation by global convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "cascaded recurrent neural networks for hyperspectral image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bs nets an end to end framework for band selection of hyperspectral image")
  Member "dense semantic labeling of subdecimeter resolution images with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)