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: "how useful is region based classification of remote sensing images in a deep learning framework"
  Abstract: In this paper, we investigate the impact of segmentation algorithms as a preprocessing step for classification of remote sensing images in a deep learning framework. Especially, we address the issue of segmenting the image into regions to be classified using pre-trained deep neural networks as feature extractors for an SVM-based classifier. An efficient segmentation as a preprocessing step helps learning by adding a spatially-coherent structure to the data. Therefore, we compare algorithms producing superpixels with more traditional remote sensing segmentation algorithms and measure the variation in terms of classification accuracy. We establish that superpixel algorithms allow for a better classification accuracy as a homogenous and compact segmentation favors better generalization of the training samples.

Connected through hyperedge HE-1 (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-2 (induced by source paper "beyond rgb very high resolution urban remote sensing with multimodal deep networks"):
  with members:
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "adversarial examples for semantic segmentation and object detection"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "two stream convolutional neural network for accurate rgb d fingertip detection using depth and edge information"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "refinenet multi path refinement networks for high resolution 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)