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 crowd counting via dynamic temporal modeling"
  Abstract: Crowd counting aims to count the number of instantaneous people in a crowded space, which plays an increasingly important role in the field of public safety. More and more researchers have already proposed many promising solutions to the crowd counting task on the image. With the continuous extension of the application of crowd counting, how to apply the technique to video content has become an urgent problem. At present, although researchers have collected and labeled some video clips, less attention has been drawn to the spatiotemporal characteristics of videos. In order to solve this problem, this paper proposes a novel framework based on dynamic temporal modeling of the relationship between video frames. We model the relationship between adjacent features by constructing a set of dilated residual blocks for crowd counting task, with each phase having an expanded set of time convolutions to generate an initial prediction which is then improved by the next prediction. We extract features from the density map as we find the adjacent density maps share more similar information than original video frames. We also propose a smaller basic network structure to balance the computational cost with a good feature representation. We conduct experiments using the proposed framework on five crowd counting datasets and demonstrate its superiority in terms of effectiveness and efficiency over previous approaches.

Connected through hyperedge HE-1 (induced by source paper "a real time deep network for crowd counting"):
  with members:
    - "fast video crowd counting with a temporal aware network"
    - "adaptive scenario discovery for crowd counting"
    - "top down feedback for crowd counting convolutional neural network"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "crowd counting via scale adaptive convolutional neural network"

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)