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: "background subtraction with real time semantic segmentation"
  Abstract: Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$. The BGS segmenter $\mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $\mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $\mathcal{B}$ and $\mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $\mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $\mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization.

Connected through hyperedge HE-1 (induced by source paper "moving objects detection with a moving camera a comprehensive review"):
  with members:
    - "deeppbm deep probabilistic background model estimation from video sequences"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "combining background subtraction algorithms with convolutional neural network"
    - "background subtraction in real applications challenges current models and future directions"
    - "learning multi scale features for foreground segmentation"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "are object detection assessment criteria ready for maritime computer vision"

Connected through hyperedge HE-2 (induced by source paper "graphbgs background subtraction via recovery of graph signals"):
  with members:
    - "background subtraction in real applications challenges current models and future directions"
    - "a fully convolutional neural network for background subtraction of unseen videos"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "foreground segmentation using convolutional neural networks for multiscale feature encoding"
    - "a robust background initialization algorithm with superpixel motion detection"
    - "mask r cnn"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (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"
    - "how far can you get by combining change detection algorithms"
    - "rethinking atrous convolution for semantic image segmentation"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image 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)