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 anomaly detection in crowded scenes via cell based analysis of foreground speed size and texture"
  Abstract: A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into non-overlapping cells, followed by extracting features based on motion, size and texture from each cell. Each feature type is independently analysed for the presence of an anomaly. Unlike most methods, a refined estimate of object motion is achieved by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown code-book, with the number of entries in the codebook selected in an online fashion. Experiments on the recently published UCSD Anomaly Detection dataset show that the proposed method obtains considerably better results than three recent approaches: MPPCA, social force, and mixture of dynamic textures (MDT). The proposed method is also several orders of magnitude faster than MDT, the next best performing method.

Connected through hyperedge HE-1 (induced by source paper "activity recognition based on a magnitude orientation stream network"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "efficient two stream motion and appearance 3d cnns for video classification"
    - "unsupervised learning of video representations using lstms"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "towards good practices for very deep two stream convnets"

Connected through hyperedge HE-2 (induced by source paper "abnormal event detection in videos using spatiotemporal autoencoder"):
  with members:
    - "learning temporal regularity in video sequences"
    - "spatio temporal video autoencoder with differentiable memory"
    - "context aware cnns for person head detection"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-3 (induced by source paper "fully convolutional neural network for fast anomaly detection in crowded scenes"):
  with members:
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "deepproposal hunting objects by cascading deep convolutional layers"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in videos"
    - "fully convolutional networks for semantic segmentation"
    - "overfeat integrated recognition localization and detection using convolutional networks"
    - "fast image scanning with deep max pooling convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning deep representations of appearance and motion for anomalous event detection"):
  with members:
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in videos"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "log euclidean bag of words for human action recognition"):
  with members:
    - "on robust face recognition via sparse coding the good the bad and the ugly"
    - "spatio temporal covariance descriptors for action and gesture recognition"
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"

Connected through hyperedge HE-6 (induced by source paper "improved foreground detection via block based classifier cascade with probabilistic decision integration"):
  with members:
    - "shadow detection a survey and comparative evaluation of recent methods"
    - "a low complexity algorithm for static background estimation from cluttered image sequences in surveillance contexts"

Connected through hyperedge HE-7 (induced by source paper "spatio temporal covariance descriptors for action and gesture recognition"):
  with members:
    - "sparse coding and dictionary learning for symmetric positive definite matrices a kernel approach"
    - "k tangent spaces on riemannian manifolds for improved pedestrian detection"

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)