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 "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics"):
  Abstract: Human action Recognition for unknown views, is a challenging task. We propose a deep view-invariant human action recognition framework, which is a novel integration of two important action cues: motion and shape temporal dynamics (STD). The motion stream encapsulates the motion content of action as RGB Dynamic Images (RGB-DIs), which are generated by Approximate Rank Pooling (ARP) and processed by using fine-tuned InceptionV3 model. The STD stream learns long-term view-invariant shape dynamics of action using a sequence of LSTM and Bi-LSTM learning models. Human Pose Model (HPM) generates view-invariant features of structural similarity index matrix (SSIM) based key depth human pose frames. The final prediction of the action is made on the basis of three types of late fusion techniques i.e. maximum (max), average (avg) and multiply (mul), applied on individual stream scores. To validate the performance of the proposed novel framework, the experiments are performed using both cross-subject and cross-view validation schemes on three publically available benchmarks-NUCLA multi-view dataset, UWA3D-II Activity dataset and NTU RGB-D Activity dataset. Our algorithm outperforms existing state-of-the-arts significantly, which is measured in terms of recognition accuracy, receiver operating characteristic (ROC) curve and area under the curve (AUC).

Includes members:
  Member "action recognition for depth video using multi view dynamic images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dmcl distillation multiple choice learning for multimodal action recognition")
  Member "compressed video action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")
  Member "temporal 3d convnets new architecture and transfer learning for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer")
  Member "learning human pose models from synthesized data for robust rgb d action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial attack on skeleton based human action recognition")
  Member "skepxels spatio temporal image representation of human skeleton joints for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finehand learning hand shapes for american sign language recognition")
  Member "action recognition with coarse to fine deep feature integration and asynchronous fusion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "three stream fusion network for first person interaction recognition")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "learning a deep model for human action recognition from novel viewpoints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

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)