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: "hyperspectral image classification with markov random fields and a convolutional neural network"
  Abstract: This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent and update the class labels of all pixel vectors using    $\alpha $   -expansion min-cut-based algorithm. Compared with the other state-of-the-art methods, the classification method achieves better performance on one synthetic data set and two benchmark HSI data sets in a number of experimental settings.

Connected through hyperedge HE-1 (induced by source paper "hyperspectral image classification with context aware dynamic graph convolutional network"):
  with members:
    - "simultaneous spectral spatial feature selection and extraction for hyperspectral images"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adaptive graph convolutional neural networks"
    - "large scale learnable graph convolutional networks"
    - "non local neural networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-2 (induced by source paper "1d convolutional capsule network for hyperspectral image classification"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "3 d deep learning approach for remote sensing image classification"
    - "spectral spatial classification of hyperspectral images three tricks and a new learning setting"
    - "dynamic routing between capsules"
    - "a convolutional neural network for modelling sentences"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

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"
    - "3 d deep learning approach for remote sensing image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "contextual deep cnn based hyperspectral classification"

Connected through hyperedge HE-4 (induced by source paper "multiscale principle of relevant information for hyperspectral image classification"):
  with members:
    - "multivariate extension of matrix based renyi s alpha order entropy functional"
    - "opening the black box of deep neural networks via information"
    - "spatial aware dictionary learning for hyperspectral image classification"

Connected through hyperedge HE-5 (induced by source paper "the history began from alexnet a comprehensive survey on deep learning approaches"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "a deep convolutional neural network for background subtraction"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "deep reinforcement learning an overview"

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)