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: "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"
  Abstract: This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods.

Connected through hyperedge HE-1 (induced by source paper "bs nets an end to end framework for band selection of hyperspectral image"):
  with members:
    - "hypergraph p laplacian regularization for remotely sensed image recognition"
    - "generative adversarial networks an overview"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "attention is all you need"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "feature extraction for hyperspectral imagery the evolution from shallow to deep"):
  with members:
    - "simultaneous spectral spatial feature selection and extraction for hyperspectral images"
    - "orsim detector a novel object detection framework in optical remote sensing imagery using spatial frequency channel features"
    - "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"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "active transfer learning network a unified deep joint spectral spatial feature learning model for hyperspectral image classification"
    - "joint progressive learning from high dimensional data for multi label classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-3 (induced by source paper "feature extraction and classification based on spatial spectral convlstm neural network for hyperspectral images"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "dynamic routing between capsules"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "training very deep networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "sequence to sequence learning with neural networks"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-4 (induced by source paper "a cnn based spatial feature fusion algorithm for hyperspectral imagery classification"):
  with members:
    - "spectral spatial feature extraction and classification by ann supervised with center loss in hyperspectral imagery"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "tensorflow a system for large scale machine learning"
    - "deep learning in neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "hyperspectral image classification with deep metric learning and conditional random field"):
  with members:
    - "a cnn based spatial feature fusion algorithm for hyperspectral imagery classification"
    - "convolutional crfs for semantic segmentation"
    - "superpca a superpixelwise pca approach for unsupervised feature extraction of hyperspectral imagery"
    - "spectral spatial feature extraction and classification by ann supervised with center loss in hyperspectral imagery"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "caffe convolutional architecture for fast feature embedding"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (induced by source paper "learned scalable image compression with bidirectional context disentanglement network"):
  with members:
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "generative adversarial networks for extreme learned image compression"
    - "conditional probability models for deep image compression"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "squeeze and excitation networks"
    - "learning to inpaint for image compression"
    - "full resolution image compression with recurrent neural networks"
    - "variable rate image compression with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "cascaded recurrent neural networks for hyperspectral image classification"):
  with members:
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "recurrent network models for human dynamics"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "spectral spatial feature extraction and classification by ann supervised with center loss in hyperspectral imagery"):
  with members:
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "facenet a unified embedding for face recognition and clustering"

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)