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: "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
  Abstract: Abstract   Benchmark datasets are critical for developing, evaluating, and comparing remote sensing image retrieval (RSIR) approaches. However, current benchmark datasets are deficient in that (1) they were originally collected for land use/land cover classification instead of RSIR; (2) they are relatively small in terms of the number of classes as well as the number of images per class which makes them unsuitable for developing deep learning based approaches; and (3) they are not appropriate for RSIR due to the large amount of background present in the images. These limitations restrict the development of novel approaches for RSIR, particularly those based on deep learning which require large amounts of training data. We therefore present a new large-scale remote sensing dataset termed “PatternNet” that was collected specifically for RSIR. PatternNet was collected from high-resolution imagery and contains 38 classes with 800 images per class. Significantly, PatternNet’s large scale makes it suitable for developing novel, deep learning based approaches for RSIR. We use PatternNet to evaluate the performance of over 35 RSIR methods ranging from traditional handcrafted feature based methods to recent, deep learning based ones. These results serve as a baseline for future research on RSIR.

Connected through hyperedge HE-1 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "a novel multi attention driven system for multi label remote sensing image classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "building instance classification using street view images"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-2 (induced by source paper "bigearthnet deep learning models with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "building instance classification using street view images"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "enhancing remote sensing image retrieval using a triplet deep metric learning network"):
  with members:
    - "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"
    - "visual descriptors for content based retrieval of remote sensing images"
    - "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-4 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "cross view image matching for geo localization in urban environments"

Connected through hyperedge HE-5 (induced by source paper "bigearthnet a large scale benchmark archive for remote sensing image understanding"):
  with members:
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "rsi cb a large scale remote sensing image classification benchmark via crowdsource data"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (induced by source paper "an entropic optimal transport loss for learning deep neural networks under label noise in remote sensing images"):
  with members:
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
    - "binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"
    - "high resolution multispectral dataset for semantic segmentation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning aerial image segmentation from online maps"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"

Connected through hyperedge HE-7 (induced by source paper "testing the efficient network training entr hypothesis initially reducing training image size makes convolutional neural network training for image recognition tasks more efficient"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "snapshot ensembles train 1 get m for free"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "densely connected convolutional networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "region convolutional features for multi label remote sensing image retrieval"):
  with members:
    - "learning low dimensional convolutional neural networks for high resolution 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"

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)