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: "video shot boundary detection using motion activity descriptor"
  Abstract: Abstract —This paper focus on the study of the motion activity descriptor for shot boundary detection in video sequences. We interest in the validation of this descriptor in the aim of its real time implementation with reasonable high performances in shot boundary detection. The motion activity information is extracted in uncompressed domain based on adaptive rood pattern search (ARPS) algorithm. In this context, the motion activity descriptor was applied for different video sequence. Index Terms —Motion activity, MPEG-7, video segmentation, ARPS, Block-matching. —————————— —————————— 1. I NTRODUCTION n recent years, the development of software and hardware technology has enabled the creation of a large amount of digital video content. Video segmentation based on motion [1] is a new research area. Motion is a salient feature in video, in addition to other typical image features such as color, shape and texture. Video shot boundary detection is a fundamental step in video indexing and retrieval, and in general video data management. The general objectives are to segment a given video sequence into its constituent shots, and to identify and classify the different shot transitions in the sequence [10]. Different algorithms have been proposed, for instance, based on simple color histograms [11, 12], pixel color differences [13], color ratio histograms [14], edges [15], and motion [16– 18]. In this work, we study the problem of video partitioning using a motion-based approach. In this study we interest in the validation of the motion activity descriptor in the aim of its possible real time implementation. We have applied this descriptor for different video sequence. We have considered its application for shot boundary detection. The rest of this paper is organized as follows: in section 2, we present the motion activity descriptor specification. In section 3 we focus on the study of the motion activity descriptor for shot boundary detection in video sequences. The different experimentations and the evolution results of the motion activity are presented in section 4.

Connected through hyperedge HE-1 (induced by source paper "transnet a deep network for fast detection of common shot transitions"):
  with members:
    - "fast video shot transition localization with deep structured models"
    - "ridiculously fast shot boundary detection with fully convolutional neural networks"
    - "large scale fast and accurate shot boundary detection through spatio temporal convolutional neural networks"
    - "wavenet a generative model for raw audio"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "a novel approach for shot boundary detection in videos"):
  with members:
    - "content based video retrieval systems"
    - "a tutorial on spectral clustering"

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.OH(Other Computer Science)