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: "targeted change detection in remote sensing images"
  Abstract: Recent developments in the remote sensing systems and image processing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images (so called targeted change detection). In this paper we propose a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remote sensing images. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.

Connected through hyperedge HE-1 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-2 (induced by source paper "boundary loss for remote sensing imagery semantic segmentation"):
  with members:
    - "superpixel enhanced pairwise conditional random field for semantic segmentation"
    - "beyond the pixel wise loss for topology aware delineation"
    - "meta learning for resampling recommendation systems"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "large scale shape retrieval with sparse 3d convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning to approximate directional fields defined over 2d planes"):
  with members:
    - "procedural synthesis of remote sensing images for robust change detection with neural networks"
    - "monocular 3d object detection via geometric reasoning on keypoints"
    - "boundary loss for remote sensing imagery semantic segmentation"
    - "vectorization of line drawings via polyvector fields"
    - "perceptual deep depth super resolution"
    - "abc a big cad model dataset for geometric deep learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "from satellite imagery to disaster insights"):
  with members:
    - "building damage annotation on post hurricane satellite imagery based on convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet 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)