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: "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
  Abstract: Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with high spectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of the hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high-dimension problem and explore abundance information, this paper presents a general end-to-end 2-D convolutional neural network (CNN) framework for hyperspectral image CD (HSI-CD). The main contributions of this paper are threefold: 1) mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multisource information; 2) 2-D CNN is designed to learn the discriminative features effectively from the multisource data at a higher level and enhance the generalization ability of the proposed CD algorithm; and 3) the new HSI-CD data set is designed for objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate that the proposed method outperforms most of the state of the arts.

Connected through hyperedge HE-1 (induced by source paper "spatial spectral residual network for hyperspectral image super resolution"):
  with members:
    - "locality and structure regularized low rank representation for hyperspectral image classification"
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "channel wise and spatial feature modulation network for single image super resolution"
    - "wide activation for efficient and accurate image super resolution"
    - "residual dense network for image super resolution"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-2 (induced by source paper "from w net to cdgan bitemporal change detection via deep learning techniques"):
  with members:
    - "psgan a generative adversarial network for remote sensing image pan sharpening"
    - "road extraction by deep residual u net"
    - "remote sensing image fusion based on two stream fusion network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "memory augmented temporal dynamic learning for action recognition"):
  with members:
    - "motion appearance co memory networks for video question answering"
    - "learning dynamic memory networks for object tracking"
    - "memory warps for learning long term online video representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lattice long short term memory for human action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"
    - "temporal segment networks towards good practices for deep action recognition"

Connected through hyperedge HE-4 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification"):
  with members:
    - "structured priors for sparse representation based hyperspectral image classification"
    - "spatial aware dictionary learning for hyperspectral image classification"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

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)