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 "robust 3d action recognition through sampling local appearances and global distributions"):
  Abstract: 3D action recognition has broad applications in human-computer interaction and intelligent surveillance. However, recognizing similar actions remains challenging since previous literature fails to capture motion and shape cues effectively from noisy depth data. In this paper, we propose a novel two-layer Bag-of-Visual-Words (BoVW) model, which suppresses the noise disturbances and jointly encodes both motion and shape cues. First, background clutter is removed by a background modeling method that is designed for depth data. Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs. In the first layer of our model, a multi-scale 3D local steering kernel (M3DLSK) descriptor is proposed to describe local appearances of cuboids around motion-based STIPs. In the second layer, a spatial-temporal vector (STV) descriptor is proposed to describe the spatial-temporal distributions of shape-based STIPs. Using the Bag-of-Visual-Words (BoVW) model, motion and shape cues are combined to form a fused action representation. Our model performs favorably compared with common STIP detection and description methods. Thorough experiments verify that our model is effective in distinguishing similar actions and robust to background clutter, partial occlusions and pepper noise.

Includes members:
  Member "elastic functional coding of riemannian trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fitting comparison and alignment of trajectories on positive semi definite matrices with application to action recognition")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "differential recurrent neural networks for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finehand learning hand shapes for american sign language recognition")
  Member "histogram of oriented principal components for cross view action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gimme signals discriminative signal encoding for multimodal activity recognition")
  Member "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi view region adaptive multi temporal dmm and rgb action recognition")
  Member "mining mid level features for action recognition based on effective skeleton representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning approach for real time 3d human action recognition from skeletal data")
  Member "learning human activities and object affordances from rgb d videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic graph convolutional networks")
  Member "unstructured human activity detection from rgbd images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "human action recognition and assessment via deep neural network self organization")

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)