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: "multiple object tracking in cluttered and crowded public spaces"
  Abstract: This paper addresses the problem of tracking moving objects of variable appearance in challenging scenes rich with features and texture. Reliable tracking is of pivotal importance in surveillance applications. It is made particularly difficult by the nature of objects encountered in such scenes: these too change in appearance and scale, and are often articulated (e.g. humans). We propose a method which uses fast motion detection and segmentation as a constraint for both building appearance models and their robust propagation (matching) in time. The appearance model is based on sets of local appearances automatically clustered using spatio-kinetic similarity, and is updated with each new appearance seen. This integration of all seen appearances of a tracked object makes it extremely resilient to errors caused by occlusion and the lack of permanence of due to low data quality, appearance change or background clutter. These theoretical strengths of our algorithm are empirically demonstrated on two hour long video footage of a busy city marketplace.

Connected through hyperedge HE-1 (induced by source paper "track everything limiting prior knowledge in online multi object recognition"):
  with members:
    - "mot16 a benchmark for multi object tracking"
    - "understanding data augmentation for classification when to warp"
    - "deep learning in neural networks"
    - "fast simple and accurate handwritten digit classification by training shallow neural network classifiers with the extreme learning machine algorithm"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "enhanced image classification with a fast learning shallow convolutional neural network"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-2 (induced by source paper "automatic vehicle tracking and recognition from aerial image sequences"):
  with members:
    - "groupwise registration of aerial images"
    - "discriminative extended canonical correlation analysis for pattern set matching"
    - "matching objects across the textured smooth continuum"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"

Connected through hyperedge HE-3 (induced by source paper "the adaptable buffer algorithm for high quantile estimation in non stationary data streams"):
  with members:
    - "viewpoint distortion compensation in practical surveillance systems"
    - "stream quantiles via maximal entropy histograms"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"

Connected through hyperedge HE-4 (induced by source paper "viewpoint distortion compensation in practical surveillance systems"):
  with members:
    - "the adaptable buffer algorithm for high quantile estimation in non stationary data streams"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"

Connected through hyperedge HE-5 (induced by source paper "two maximum entropy based algorithms for running quantile estimation in non stationary data streams"):
  with members:
    - "stream quantiles via maximal entropy histograms"
    - "contextually learnt detection of unusual motion based behaviour in crowded public spaces"

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)