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 "rpca kfe key frame extraction for consumer video based robust principal component analysis"):
  Abstract: Key frame extraction algorithms consider the prob- lem of selecting a subset of the most informative frames from a video to summarize its content. Several applications such as video summarization, search, indexing and prints from video can benefit from extracted key frames of the video under consideration. Most approaches in this class of algorithms work directly with the input video dataset, without considering the underlying low rank structure of the dataset. Other algorithms exploit the low rank component only; ignoring the other key information in the video. In this paper, a novel key frame extraction framework based on Robust Principal Component Analysis (RPCA) is proposed. Furthermore, we target the chal- lenging application of extracting key frames from unstructured consumer videos. The proposed framework is motivated by the observation that RPCA decomposes an input data into (a) a low rank component that reveals the systematic information across the elements of the dataset and (b) a set of sparse components each of which containing distinct information about each element in the same dataset. The two information types are combined into a single '1-norm based non-convex optimization problem to extract the desired number of key frames. Moreover, we develop a novel iterative algorithm to solve this optimization problem. The proposed RPCA-based framework does not require shot(s) detection, segmentation, or semantic understanding of the underlying video. Finally, experiments are performed on a variety of consumer videos. A comparison of the results obtained by our method with the ground truth and related state-of-the-art algorithms clearly indicates the viability of the proposed RPCA- based framework.

Includes members:
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "median k flats for hybrid linear modeling with many outliers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust subspace recovery layer for unsupervised anomaly detection")

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)