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: "shadow detection a survey and comparative evaluation of recent methods"
  Abstract: This paper presents a survey and a comparative evaluation of recent techniques for moving cast shadow detection. We identify shadow removal as a critical step for improving object detection and tracking. The survey covers methods published during the last decade, and places them in a feature-based taxonomy comprised of four categories: chromacity, physical, geometry and textures. A selection of prominent methods across the categories is compared in terms of quantitative performance measures (shadow detection and discrimination rates, colour desaturation) as well as qualitative observations. Furthermore, we propose the use of tracking performance as an unbiased approach for determining the practical usefulness of shadow detection methods. The evaluation indicates that all shadow detection approaches make different contributions and all have individual strength and weaknesses. Out of the selected methods, the geometry-based technique has strict assumptions and is not generalisable to various environments, but it is a straightforward choice when the objects of interest are easy to model and their shadows have different orientation. The chromacity based method is the fastest to implement and run, but it is sensitive to noise and less effective in low saturated scenes. The physical method improves upon the accuracy of the chromacity method by adapting to local shadow models, but fails when the spectral properties of the objects are similar to that of the background. The small-region texture based method is especially robust for pixels whose neighbourhood is textured, but may take longer to implement and is the most computationally expensive. The large-region texture based method produces the most accurate results, but has a significant computational load due to its multiple processing steps.

Connected through hyperedge HE-1 (induced by source paper "semantic referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  with members:
    - "location augmentation for cnn"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep learning in remote sensing a review"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "explaining trained neural networks with semantic web technologies first steps"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "relating input concepts to convolutional neural network decisions"
    - "what does explainable ai really mean a new conceptualization of perspectives"

Connected through hyperedge HE-2 (induced by source paper "enhanced mixtures of part model for human pose estimation"):
  with members:
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deeppose human pose estimation via deep neural networks"
    - "beyond physical connections tree models in human pose estimation"
    - "learning human pose estimation features with convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "improvements and experiments of a compact statistical background model"):
  with members:
    - "background modeling using adaptive pixelwise kernel variances in a hybrid feature space"

Connected through hyperedge HE-4 (induced by source paper "improved foreground detection via block based classifier cascade with probabilistic decision integration"):
  with members:
    - "improved anomaly detection in crowded scenes via cell based analysis of foreground speed size and texture"
    - "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)