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: "a video analysis method on wanfang dataset via deep neural network"
  Abstract: The topic of object detection has been largely improved recently, especially with the development of convolutional neural network. However, there still exist a lot of challenging cases, such as small object, compact and dense or highly overlapping object. Existing methods can detect multiple objects wonderfully, but because of the slight changes between frames, the detection effect of the model will become unstable, the detection results may result in dropping or increasing the object. In the pedestrian flow detection task, such phenomenon can not accurately calculate the flow. To solve this problem, in this paper, we describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Our work is to extract a video clip and solve this frame of clips efficiently. More specfically, our algorithm includes two stages: judge method and optimization method. The judge can set a maximum threshold for better results under the model, the threshold value corresponds to the upper limit of the algorithm with better detection results. The optimization method to solve detection jitter problem. Because of the occurrence of frame hopping in the video, and it will result in the generation of video fragments discontinuity. We use optimization algorithm to get the key value, and then the detection result value of index is replaced by key value to stabilize the change of detection result sequence. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset and our own test dataset for YOLOv3-Abnormal Number Version(YOLOv3-ANV), which is 5.4% average improvement compared with existing methods. Also, video above the threshold value can be obtained for further analysis. Spontaneously, our work also can used for pedestrians flow detection and pedestrian alarm tasks.

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)