human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the selective and invariant representation of dcnn for high resolution remote sensing image recognition"):
  Abstract: Human vision possesses strong invariance in image recognition. The cognitive capability of deep convolutional neural network (DCNN) is close to the human visual level because of hierarchical coding directly from raw image. Owing to its superiority in feature representation, DCNN has exhibited remarkable performance in scene recognition of high-resolution remote sensing (HRRS) images and classification of hyper-spectral remote sensing images. In-depth investigation is still essential for understanding why DCNN can accurately identify diverse ground objects via its effective feature representation. Thus, we train the deep neural network called AlexNet on our large scale remote sensing image recognition benchmark. At the neuron level in each convolution layer, we analyze the general properties of DCNN in HRRS image recognition by use of a framework of visual stimulation-characteristic response combined with feature coding-classification decoding. Specifically, we use histogram statistics, representational dissimilarity matrix, and class activation mapping to observe the selective and invariance representations of DCNN in HRRS image recognition. We argue that selective and invariance representations play important roles in remote sensing images tasks, such as classification, detection, and segment. Also selective and invariance representations are significant to design new DCNN liked models for analyzing and understanding remote sensing images.

Includes members:
  Member "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images")
  Member "towards better exploiting convolutional neural networks for remote sensing scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolution neural network architecture learning for remote sensing scene classification")
  Member "deep neural networks predict hierarchical spatio temporal cortical dynamics of human visual object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "brain like object recognition with high performing shallow recurrent anns")
  Member "learning multi scale deep features for high resolution satellite image classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond rgb very high resolution urban remote sensing with multimodal deep networks")
  Member "understanding neural networks through deep visualization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adapting object detectors with conditional domain normalization")
  Member "land use classification in remote sensing images by convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow")
  Member "convergent learning do different neural networks learn the same representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "classifying the classifier dissecting the weight space of neural networks")
  Member "understanding intra class knowledge inside cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fashion minimal edits for outfit improvement")

Question: Please tell me which class the source paper 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)