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: "learning multi scale deep features for high resolution satellite image classification"
  Abstract: In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully-connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multi-scale satellite images are fed into their corresponding SPP-nets respectively to extract multi-scale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult datasets show that the proposed method achieves favorable performance compared to other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "beyond rgb very high resolution urban remote sensing with multimodal deep networks"):
  with members:
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "on the selective and invariant representation of dcnn for high resolution remote sensing image recognition"):
  with members:
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition"
    - "understanding neural networks through deep visualization"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "convergent learning do different neural networks learn the same representations"
    - "understanding intra class knowledge inside cnn"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-3 (induced by source paper "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"):
  with members:
    - "adaptive deep pyramid matching for remote sensing scene classification"

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)