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: "moving object detection by detecting contiguous outliers in the low rank representation"
  Abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.

Connected through hyperedge HE-1 (induced by source paper "on learning vehicle detection in satellite video"):
  with members:
    - "clustered object detection in aerial images"
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "multi cue vehicle detection for semantic video compression in georegistered aerial videos"
    - "yolov3 an incremental improvement"
    - "detecting tiny moving vehicles in satellite videos"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "object oriented dynamics learning through multi level abstraction"):
  with members:
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational deep reinforcement learning"
    - "deep learning for generic object detection a survey"
    - "relational inductive biases deep learning and graph networks"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "contingency aware exploration in reinforcement learning"
    - "visual interaction networks"

Connected through hyperedge HE-3 (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"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "an integrated image filter for enhancing change detection results"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "efficient video object segmentation via network modulation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "conditional random fields as recurrent neural networks"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-5 (induced by source paper "simultaneous detection and removal of dynamic objects in multi view images"):
  with members:
    - "image inpainting for irregular holes using partial convolutions"
    - "generative image inpainting with contextual attention"
    - "deep image prior"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "a diffusion and clustering based approach for finding coherent motions and understanding crowd scenes"
    - "video inpainting of complex scenes"

Connected through hyperedge HE-6 (induced by source paper "error bounded foreground and background modeling for moving object detection in satellite videos"):
  with members:
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "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"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "moving object detection under discontinuous change in illumination using tensor low rank and invariant sparse decomposition"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "total variation regularized tensor rpca for background subtraction from compressive measurements"
    - "background subtraction via generalized fused lasso foreground modeling"
    - "corola a sequential solution to moving object detection using low rank approximation"
    - "novel methods for multilinear data completion and de noising based on tensor svd"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "locality and structure regularized low rank representation for hyperspectral image classification"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "structured priors for sparse representation based hyperspectral image classification"
    - "spatial aware dictionary learning for hyperspectral image classification"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"

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)