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 far can you get by combining change detection algorithms"
  Abstract: Given the existence of many change detection algorithms, each with its own peculiarities and strengths, we propose a combination strategy, that we termed IUTIS (In Unity There Is Strength), based on a genetic Programming framework. This combination strategy is aimed at leveraging the strengths of the algorithms and compensate for their weakness. In this paper we show our findings in applying the proposed strategy in two different scenarios. The first scenario is purely performance-based. The second scenario performance and efficiency must be balanced. Results demonstrate that starting from simple algorithms we can achieve comparable results with respect to more complex state-of-the-art change detection algorithms, while keeping the computational complexity affordable for real-time applications.

Connected through hyperedge HE-1 (induced by source paper "bsuv net a fully convolutional neural network for background subtraction of unseen videos"):
  with members:
    - "combining background subtraction algorithms with convolutional neural network"
    - "3dfr a swift 3d feature reductionist framework for scene independent change detection"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "background subtraction with real time semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"

Connected through hyperedge HE-3 (induced by source paper "learning multi scale features for foreground segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "a deep convolutional neural network for background subtraction"
    - "rethinking atrous convolution for semantic image segmentation"
    - "instance normalization the missing ingredient for fast stylization"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "a fully convolutional neural network for background subtraction of unseen videos"):
  with members:
    - "combining background subtraction algorithms with convolutional neural network"
    - "learning multi scale features for foreground segmentation"
    - "wisenetmd motion detection using dynamic background region analysis"
    - "rethinking atrous convolution for semantic image segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "deeppbm deep probabilistic background model estimation from video sequences"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "background subtraction via fast robust matrix completion"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "learning to measure change fully convolutional siamese metric networks for scene change detection"):
  with members:
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "depth adaptive deep neural network for semantic segmentation"
    - "arcface additive angular margin loss for deep face recognition"
    - "segmentation aware convolutional networks using local attention masks"
    - "deformable convolutional networks"
    - "semantic instance segmentation via deep metric learning"
    - "learning deep structured network for weakly supervised change detection"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-7 (induced by source paper "a foreground inference network for video surveillance using multi view receptive field"):
  with members:
    - "a deep convolutional neural network for background subtraction"
    - "instance sensitive fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "understanding convolutional neural networks with a mathematical model"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "xception deep learning with depthwise separable convolutions"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "background subtraction with real time semantic segmentation"):
  with members:
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)