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 "randomized tensor ring decomposition and its application to large scale data reconstruction"):
  Abstract: Dimensionality reduction is an essential technique for multiway large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are 4–25 times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to the other randomized algorithms.

Includes members:
  Member "a practical randomized cp tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "randomized cp tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust deep networks with randomized tensor regression layers")
  Member "tensor ring decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")
  Member "era of big data processing a new approach via tensor networks and tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a parallel sparse tensor benchmark suite on cpus and gpus")
  Member "decomposition of big tensors with low multilinear rank":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")

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.NA(Numerical Analysis)