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 "dynamical textures modeling via joint video dictionary learning"):
  Abstract: Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DTs) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning ( JVDL ), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying “states”, we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of  JVDL , we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both the sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned  JVDL  parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed  JVDL  approach in comparison with the state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

Includes members:
  Member "extrinsic methods for coding and dictionary learning on grassmann manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized visual information analysis via tensorial algebra")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online structured sparsity based moving object detection from satellite videos")
  Member "an adaptive dictionary learning approach for modeling dynamical textures":
    also connected through other hyperedges:
      (none)
  Member "dynamic template tracking and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep discriminative model for video classification")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nestedvae isolating common factors via weak supervision")

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.SY(Systems and Control)