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 "scalable and compact 3d action recognition with approximated rbf kernel machines"):
  Abstract: Despite the recent deep learning (DL) revolution, kernel machines still remain powerful methods for action recognition. DL has brought the use of large datasets and this is typically a problem for kernel approaches, which are not scaling up efficiently due to kernel Gram matrices. Nevertheless, kernel methods are still attractive and more generally applicable since they can equally manage different sizes of the datasets, also in cases where DL techniques show some limitations. This work investigates these issues by proposing an explicit approximated representation that, together with a linear model, is an equivalent, yet scalable, implementation of a kernel machine. Our approximation is directly inspired by the exact feature map that is induced by an RBF Gaussian kernel but, unlike the latter, it is finite dimensional and very compact. We justify the soundness of our idea with a theoretical analysis which proves the unbiasedness of the approximation, and provides a vanishing bound for its variance, which is shown to decrease much rapidly than in alternative methods in the literature. In a broad experimental validation, we assess the superiority of our approximation in terms of 1) ease and speed of training, 2) compactness of the model, and 3) improvements with respect to the state-of-the-art performance.

Includes members:
  Member "a compact kernel approximation for 3d action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minimal entropy correlation alignment for unsupervised deep domain adaptation")
  Member "a new representation of skeleton sequences for 3d action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "joint distance maps based action recognition with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep independently recurrent neural network indrnn")
  Member "kernelized covariance for action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning unbiased representations via mutual information backpropagation")
  Member "ntu rgb d a large scale dataset for 3d human activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "spatio temporal lstm with trust gates for 3d human action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "action recognition based on joint trajectory maps using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on 3d skeleton based action recognition using learning method")
  Member "deep learning on lie groups for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural graph embedding methods for natural language processing")

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)