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 "clrma compact low rank matrix approximation for data compression"):
  Abstract: Low rank matrix approximation (LRMA) is a powerful technique for signal processing and pattern analysis. However, the performance of existing LRMA-based compression methods are still limited. In this paper, we propose compact low rank matrix approximation (CLRMA), a very effective tool for data compression, which extends the LRMA by exploring both the intra- and inter-coherence of data samples simultaneously. Technically, under the assistance of prescribed orthogonal transforms (such as such as discrete cosine/wavelet transform and graph transform), the CLRMA decomposes a matrix into a product of two smaller matrices so that one consists of extremely sparse and orthogonal column vectors, and the other is a transformed coefficient matrix for reconstruction. Numerically, we formulate the CLRMA problem by minimizing the $\ell_0$-norm and orthogonality regularized approximation (or reconstruction) error, and solve it by the inexact augmented Lagrangian multiplier method. We demonstrate the efficacy of CLMRA on various types of real-world data, including 3D meshes, image datasets, videos as well as human motion capture data, that is, the proposed CLRMA, in a much more compact form, can produce comparable approximation (or reconstruction) error as LRMA. Moreover, we present a CLRMA-based compression scheme for 3D dynamic meshes, and experimental results show that it outperforms the state-of-the-art scheme to a large extend in terms of compression performance.

Includes members:
  Member "human motion capture data tailored transform coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic polygon cloud compression")
  Member "low rank modeling and its applications in image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive survey for low rank regularization")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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.MM(Multimedia)