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: "improved foreground detection via block based classifier cascade with probabilistic decision integration"
  Abstract: Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Connected through hyperedge HE-1 (induced by source paper "an open source c implementation of multi threaded gaussian mixture models k means and expectation maximisation"):
  with members:
    - "exploiting temporal information for dcnn based fine grained object classification"
    - "comparative evaluation of action recognition methods via riemannian manifolds fisher vectors and gmms ideal and challenging conditions"
    - "modelling local deep convolutional neural network features to improve fine grained image classification"
    - "on robust face recognition via sparse coding the good the bad and the ugly"
    - "mlpack a scalable c machine learning library"
    - "revisiting k means new algorithms via bayesian nonparametrics"
    - "the pitfalls of verifying floating point computations"

Connected through hyperedge HE-2 (induced by source paper "multi action recognition via stochastic modelling of optical flow and gradients"):
  with members:
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-3 (induced by source paper "summarisation of short term and long term videos using texture and colour"):
  with members:
    - "spatio temporal covariance descriptors for action and gesture recognition"

Connected through hyperedge HE-4 (induced by source paper "multi shot person re identification via relational stein divergence"):
  with members:
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "k tangent spaces on riemannian manifolds for improved pedestrian detection"

Connected through hyperedge HE-5 (induced by source paper "shadow detection a survey and comparative evaluation of recent methods"):
  with members:
    - "a low complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts"

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)