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: "complex background subtraction by pursuing dynamic spatio temporal models"
  Abstract: Although it has been widely discussed in video surveillance, background subtraction is still an open problem in the context of complex scenarios, e.g., dynamic backgrounds, illumination variations, and indistinct foreground objects. To address these challenges, we propose an effective background subtraction method by learning and maintaining an array of dynamic texture models within the spatio-temporal representations. At any location of the scene, we extract a sequence of regular video bricks, i.e., video volumes spanning over both spatial and temporal domain. The background modeling is thus posed as pursuing subspaces within the video bricks while adapting the scene variations. For each sequence of video bricks, we pursue the subspace by employing the auto regressive moving average model that jointly characterizes the appearance consistency and temporal coherence of the observations. During online processing, we incrementally update the subspaces to cope with disturbances from foreground objects and scene changes. In the experiments, we validate the proposed method in several complex scenarios, and show superior performances over other state-of-the-art approaches of background subtraction. The empirical studies of parameter setting and component analysis are presented as well.

Connected through hyperedge HE-1 (induced by source paper "instaboost boosting instance segmentation via probability map guided copy pasting"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "explicit shape encoding for real time instance segmentation"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning segmentation masks with the independence prior"
    - "srda generating instance segmentation annotation via scanning reasoning and domain adaptation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "crowdpose efficient crowded scenes pose estimation and a new benchmark"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-2 (induced by source paper "on the role and the importance of features for background modeling and foreground detection"):
  with members:
    - "robust 3d action recognition through sampling local appearances and global distributions"
    - "estimating sparse signals with smooth support via convex programming and block sparsity"
    - "deep learning in neural networks"
    - "variational inference for background subtraction in infrared imagery"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "background subtraction separating the modeling and the inference"
    - "adaptive rate compressive sensing using side information"
    - "adaptive low rank and sparse decomposition of video using compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "video processing from electro optical sensors for object detection and tracking in maritime environment a survey"):
  with members:
    - "image segmentation in video sequences a probabilistic approach"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

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)