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: "contextually learnt detection of unusual motion based behaviour in crowded public spaces"
  Abstract: In this paper we are interested in analyzing behaviour in crowded public places at the level of holistic motion. Our aim is to learn, without user input, strong scene priors or labelled data, the scope of “normal behaviour” for a particular scene and thus alert to novelty in unseen footage. The first contribution is a low-level motion model based on what we term tracklet primitives, which are scene-specific elementary motions. We propose a clustering-based algorithm for tracklet estimation from local approximations to tracks of appearance features. This is followed by two methods for motion novelty inference from tracklet primitives: (a) an approach based on a non-hierarchial ensemble of Markov chains as a means of capturing behavioural characteristics at different scales, and (b) a more flexible alternative which exhibits a higher generalizing power by accounting for constraints introduced by intentionality and goal-oriented planning of human motion in a particular scene. Evaluated on a 2 h long video of a busy city marketplace, both algorithms are shown to be successful at inferring unusual behaviour, the latter model achieving better performance for novelties at a larger spatial scale.

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 "achieving stable subspace clustering by post processing generic clustering results"):
  with members:
    - "a framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data"
    - "hallucinating optimal high dimensional subspaces"
    - "discriminative extended canonical correlation analysis for pattern set matching"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (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"
    - "multiple object tracking in cluttered and crowded public spaces"

Connected through hyperedge HE-4 (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"
    - "multiple object tracking in cluttered and crowded public spaces"

Connected through hyperedge HE-5 (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"
    - "multiple object tracking in cluttered and crowded public spaces"

Connected through hyperedge HE-6 (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"
    - "multiple object tracking in cluttered and 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)