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 "tensor sparse and low rank based submodule clustering method for multi way data"):
  Abstract: A new submodule clustering method via sparse and low-rank representation for multi-way data is proposed in this paper. Instead of reshaping multi-way data into vectors, this method maintains their natural orders to preserve data intrinsic structures, e.g., image data kept as matrices. To implement clustering, the multi-way data, viewed as tensors, are represented by the proposed tensor sparse and low-rank model to obtain its submodule representation, called a free module, which is finally used for spectral clustering. The proposed method extends the conventional subspace clustering method based on sparse and low-rank representation to multi-way data submodule clustering by combining t-product operator. The new method is tested on several public datasets, including synthetical data, video sequences and toy images. The experiments show that the new method outperforms the state-of-the-art methods, such as Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR), Ordered Subspace Clustering (OSC), Robust Latent Low Rank Representation (RobustLatLRR) and Sparse Submodule Clustering method (SSmC).

Includes members:
  Member "group invariant subspace clustering":
    also connected through other hyperedges:
      (none)
  Member "a new low rank tensor model for video completion":
    also connected through other hyperedges:
      (none)
  Member "clustering multi way data a novel algebraic approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data")
  Member "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the convergence of admm with task adaption and beyond")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably efficient exploration for rl with unsupervised learning")

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)