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 "predictively encoded graph convolutional network for noise robust skeleton based action recognition"):
  Abstract: In skeleton-based action recognition, graph convolutional networks (GCNs), which model human body skeletons using graphical components such as nodes and connections, have achieved remarkable performance recently. However, current state-of-the-art methods for skeleton-based action recognition usually work on the assumption that the completely observed skeletons will be provided. This may be problematic to apply this assumption in real scenarios since there is always a possibility that captured skeletons are incomplete or noisy. In this work, we propose a skeleton-based action recognition method which is robust to noise information of given skeleton features. The key insight of our approach is to train a model by maximizing the mutual information between normal and noisy skeletons using a predictive coding manner. We have conducted comprehensive experiments about skeleton-based action recognition with defected skeletons using NTU-RGB+D and Kinetics-Skeleton datasets. The experimental results demonstrate that our approach achieves outstanding performance when skeleton samples are noised compared with existing state-of-the-art methods.

Includes members:
  Member "actional structural graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "driver drowsiness detection using condition adaptive representation learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "snider single noisy image denoising and rectification for improving license plate recognition")
  Member "richlt activated graph convolutional network for action recognition with incomplete skeletons":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "learning graph convolutional network for skeleton based human action recognition by neural searching":
    also connected through other hyperedges:
      (none)
  Member "an attention enhanced graph convolutional lstm network for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "skeleton based action recognition with multi stream adaptive graph convolutional networks":
    also connected through other hyperedges:
      (none)
  Member "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "densefusion 6d object pose estimation by iterative dense fusion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping")

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)