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: "contextual deep cnn based hyperspectral classification"
  Abstract: In this paper, we describe a novel deep convolutional neural networks (CNN) based approach called contextual deep CNN that can jointly exploit spatial and spectral features for hyperspectral image classification. The contextual deep CNN first concurrently applies multiple 3-dimensional local convolutional filters with different sizes jointly exploiting spatial and spectral features of a hyperspectral image. The initial spatial and spectral feature maps obtained from applying the variable size convolutional filters are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through fully convolutional layers that eventually predict the corresponding label of each pixel vector. The proposed approach is tested on the Indian Pines data and performance comparison shows enhanced classification performance of the proposed approach over the current state of the art.

Connected through hyperedge HE-1 (induced by source paper "deep connections between learning from limited labels physical parameter estimation inspiration for regularization"):
  with members:
    - "symmetric block low rank layers for fully reversible multilevel neural networks"
    - "imexnet a forward stable deep neural network"
    - "fully hyperbolic convolutional neural networks"
    - "anodev2 a coupled neural ode evolution framework"
    - "leanconvnets low cost yet effective convolutional neural networks"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "is pretraining necessary for hyperspectral image classification"):
  with members:
    - "rethinking imagenet pre training"
    - "exploring the limits of weakly supervised pretraining"
    - "cross domain cnn for hyperspectral image classification"
    - "dod cnn doubly injecting object information for event recognition"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "iod cnn integrating object detection networks for event recognition"

Connected through hyperedge HE-3 (induced by source paper "3d cnn with localized residual connections for hyperspectral image classification"):
  with members:
    - "avdnet a small sized vehicle detection network for aerial visual data"
    - "hyperspectral image classification with markov random fields and a convolutional neural network"
    - "3 d deep learning approach for remote sensing image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"

Connected through hyperedge HE-4 (induced by source paper "discovering and generating hard examples for training a red tide detector"):
  with members:
    - "deep video generation prediction and completion of human action sequences"
    - "cross domain cnn for hyperspectral image classification"
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "future frame prediction for anomaly detection a new baseline"
    - "residual conv deconv grid network for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "cross domain cnn for hyperspectral image classification"):
  with members:
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "learning rich features from rgb d images for object detection and segmentation"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial networks for realistic synthesis of hyperspectral samples"):
  with members:
    - "improved training of wasserstein gans"
    - "hyperspectral cnn classification with limited training samples"
    - "inverting variational autoencoders for improved generative accuracy"
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "going deeper with contextual cnn for hyperspectral image classification"):
  with members:
    - "active deep learning for classification of hyperspectral images"
    - "shapelet based sparse representation for landcover classification of hyperspectral images"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for semantic segmentation"

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)