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 "make skeleton based action recognition model smaller faster and better"):
  Abstract: Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition. By using a lightweight network structure (i.e., 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500 FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net achieves the state-of-the-art performance on our experimental datasets: SHREC (i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released with this paper later.

Includes members:
  Member "can wifi estimate person pose":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wi2vi generating video frames from wifi csi samples")
  Member "simple yet efficient real time pose based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "skeleton based activity recognition by fusing part wise spatio temporal and attention driven residues")
  Member "spatial temporal graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "slowfast networks for video recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "skeleton based action recognition using lstm and cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep aligned convolutional neural network for skeleton based action recognition and segmentation")
  Member "vnect real time 3d human pose estimation with a single rgb camera":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "posenet3d unsupervised 3d human shape and pose estimation")

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)