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: "visual descriptors for content based retrieval of remote sensing images"
  Abstract: In this paper we present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global hand-crafted, local hand-crafted, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use/Land Cover (LandUse) dataset and 19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted. Features extracted from SatResNet-50, a residual CNN suitable fine-tuned on the RS domain, shows much better performance than a residual CNN pre-trained on multimedia scene and object images. Features extracted from NetVLAD, a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine-grained textures and objects.

Connected through hyperedge HE-1 (induced by source paper "enhancing remote sensing image retrieval using a triplet deep metric learning network"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "in defense of the triplet loss for person re identification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "land cover classification with high resolution remote sensing images using transferable deep models"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "texture characterization using shape co occurrence patterns"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"

Connected through hyperedge HE-3 (induced by source paper "image retrieval approach based on local texture information derived from predefined patterns and spatial domain information"):
  with members:
    - "color texture classification approach based on combination of primitive pattern units and statistical features"

Connected through hyperedge HE-4 (induced by source paper "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "remote sensing image scene classification benchmark and state of the art"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "land use classification using convolutional neural networks applied to ground level images"

Connected through hyperedge HE-5 (induced by source paper "learning transferable deep models for land use classification with high resolution remote sensing images"):
  with members:
    - "large scale land cover classification in gaofen 2 satellite imagery"
    - "texture characterization using shape co occurrence patterns"
    - "dota a large scale dataset for object detection in aerial images"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep features for remote sensing image matching via discriminator network"):
  with members:
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "the importance of skip connections in biomedical image segmentation"
    - "localizing and orienting street views using overhead imagery"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "going deeper with convolutions"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deep metric learning using triplet network"

Connected through hyperedge HE-7 (induced by source paper "multitask painting categorization by deep multibranch neural network"):
  with members:
    - "large age gap face verification by feature injection in deep networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "large scale classification of fine art paintings learning the right metric on the right feature"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "recognizing image style"

Connected through hyperedge HE-8 (induced by source paper "region convolutional features for multi label remote sensing image retrieval"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "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)