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 low dimensional convolutional neural networks for high resolution remote sensing image retrieval"
  Abstract: Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs) for high-resolution remote sensing image retrieval (HRRSIR). To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "deep hashing learning for visual and semantic retrieval of remote sensing images"):
  with members:
    - "supervised discrete hashing with relaxation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "exploiting deep features for remote sensing image retrieval a systematic investigation"
    - "feature learning based deep supervised hashing with pairwise labels"
    - "deep residual learning for image recognition"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "aggregated deep local features for remote sensing image retrieval"):
  with members:
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "remote sensing image scene classification benchmark and state of the art"
    - "large scale image retrieval with attentive deep local features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"
    - "neural codes for image retrieval"
    - "memory vectors for similarity search in high dimensional spaces"

Connected through hyperedge HE-3 (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"
    - "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"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "land use classification using convolutional neural networks applied to ground level images"

Connected through hyperedge HE-4 (induced by source paper "unsupervised deep features for remote sensing image matching via discriminator network"):
  with members:
    - "the importance of skip connections in biomedical image segmentation"
    - "localizing and orienting street views using overhead imagery"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "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-5 (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"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep learning in remote sensing a review"):
  with members:
    - "toward seamless multiview scene analysis from satellite to street level"
    - "look wider to match image patches with convolutional neural networks"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "lift learned invariant feature transform"
    - "a survey on object detection in optical remote sensing images"
    - "gvnn neural network library for geometric computer vision"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-7 (induced by source paper "exploiting deep features for remote sensing image retrieval a systematic investigation"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "texture characterization using shape co occurrence patterns"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "deep image retrieval learning global representations for image search"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "distributed mining of large scale remote sensing image archives on public computing infrastructures"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-8 (induced by source paper "on the selective and invariant representation of dcnn for high resolution remote sensing image recognition"):
  with members:
    - "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"
    - "learning multi scale deep features for high resolution satellite image classification"
    - "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"

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)