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: "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
  Abstract: Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this study, we apply popular concepts from traditional object tracking, namely (1) Kernelized Correlation Filters (KCF) and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation Filter based tracker (DeepHKCF) to efficiently track aerial vehicles using an adaptive multi-modal hyperspectral sensor. We address low temporal resolution by designing a single KCF-in-multiple Regions-of-Interest (ROIs) approach to cover a reasonably large area. To increase the speed of deep convolutional features extraction from multiple ROIs, we design an effective ROI mapping strategy. The proposed tracker also provides flexibility to couple with the more advanced correlation filter trackers. The DeepHKCF tracker performs exceptionally well with deep features set up in a synthetic hyperspectral video generated by the Digital Imaging and Remote Sensing Image Generation (DIRSIG) software. Additionally, we generate a large, synthetic, single-channel dataset using DIRSIG to perform vehicle classification in the Wide Area Motion Imagery (WAMI) platform. This way, the high-fidelity of the DIRSIG software is proved and a large scale aerial vehicle classification dataset is released to support studies on vehicle detection and tracking in the WAMI platform.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "generating interpretable poverty maps using object detection in satellite images"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "xview objects in context in overhead imagery"
    - "yolov3 an incremental improvement"
    - "consistent individualized feature attribution for tree ensembles"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"

Connected through hyperedge HE-3 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "xview objects in context in overhead imagery"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-4 (induced by source paper "learning to interpret satellite images in global scale using wikipedia"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images using wikipedia"
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "learning two branch neural networks for image text matching tasks"
    - "snorkel rapid training data creation with weak supervision"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"

Connected through hyperedge HE-5 (induced by source paper "online structured sparsity based moving object detection from satellite videos"):
  with members:
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "dota a large scale dataset for object detection in aerial images"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-6 (induced by source paper "cloud removal in satellite images using spatiotemporal generative networks"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "a remote sensing image dataset for cloud removal"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"

Connected through hyperedge HE-7 (induced by source paper "error bounded foreground and background modeling for moving object detection in satellite videos"):
  with members:
    - "background subtraction via generalized fused lasso foreground modeling"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "material based object tracking in hyperspectral videos benchmark and algorithms"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "object tracking in hyperspectral videos with convolutional features and kernelized correlation filter"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "geometry informed material recognition"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"

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)