human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "fast video crowd counting with a temporal aware network"):
  Abstract: Crowd counting aims to count the number of instantaneous people in a crowded space, and many promising solutions have been proposed for single image crowd counting. With the ubiquitous video capture devices in public safety field, how to effectively apply the crowd counting technique to video content has become an urgent problem. In this paper, we introduce a novel framework based on temporal aware modeling of the relationship between video frames. The proposed network contains a few dilated residual blocks, and each of them consists of the layers that compute the temporal convolutions of features from the adjacent frames to improve the prediction. To alleviate the expensive computation and satisfy the demand of fast video crowd counting, we also introduce a lightweight network to balance the computational cost with representation ability. We conduct experiments on the crowd counting benchmarks and demonstrate its superiority in terms of effectiveness and efficiency over previous video-based approaches.

Includes members:
  Member "scar spatial channel wise attention regression networks for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "learning spatial awareness to improve crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "ms tcn multi stage temporal convolutional network for action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation")
  Member "learning from synthetic data for crowd counting in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "pcc net perspective crowd counting via spatial convolutional network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "attend to count crowd counting with adaptive capacity multi scale cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crowd counting via hierarchical scale recalibration network")
  Member "crowd counting with deep structured scale integration network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nas count counting by density with neural architecture search")
  Member "composition loss for counting density map estimation and localization in dense crowds":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

Question: Please tell me which class the source paper 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)