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: "adaptive deep pyramid matching for remote sensing scene classification"
  Abstract: Convolutional neural networks (CNNs) have attracted increasing attention in the remote sensing community. Most CNNs only take the last fully-connected layers as features for the classification of remotely sensed images, discarding the other convolutional layer features which may also be helpful for classification purposes. In this paper, we propose a new adaptive deep pyramid matching (ADPM) model that takes advantage of the features from all of the convolutional layers for remote sensing image classification. To this end, the optimal fusing weights for different convolutional layers are learned from the data itself. In remotely sensed scenes, the objects of interest exhibit different scales in distinct scenes, and even a single scene may contain objects with different sizes. To address this issue, we select the CNN with spatial pyramid pooling (SPP-net) as the basic deep network, and further construct a multi-scale ADPM model to learn complementary information from multi-scale images. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "fine grained land use classification at the city scale using ground level images"):
  with members:
    - "building instance classification using street view images"
    - "large scale mapping of human activity using geo tagged videos"
    - "hidden two stream convolutional networks for action recognition"
    - "quantitative comparison of open source data for fine grain mapping of land use"
    - "webvision challenge visual learning and understanding with web data"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "disturblabel regularizing cnn on the loss layer"

Connected through hyperedge HE-2 (induced by source paper "scene classification based on multiscale convolutional neural network"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "identity mappings in deep residual networks"
    - "ssd single shot multibox detector"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "building instance classification using street view images"):
  with members:
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "places an image database for deep scene understanding"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "deep residual learning for image recognition"
    - "task driven dictionary learning for hyperspectral image classification with structured sparsity constraints"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "bidirectional convolutional lstm based spectral spatial feature learning for hyperspectral image classification"):
  with members:
    - "learning multi scale deep features for high resolution satellite image 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)