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 "compressed randomized utv decompositions for low rank approximations and big data applications"):
  Abstract: Low-rank matrix approximations play a fundamental role in numerical linear algebra and signal processing applications. This paper introduces a novel rank-revealing matrix decomposition algorithm termed Compressed Randomized UTV (CoR-UTV) decomposition along with a CoR-UTV variant aided by the power method technique. CoR-UTV is primarily developed to compute an approximation to a low-rank input matrix by making use of random sampling schemes. Given a large and dense matrix of size $m\times n$ with numerical rank $k$, where $k \ll \text{min} \{m,n\}$, CoR-UTV requires a few passes over the data, and runs in $O(mnk)$ floating-point operations. Furthermore, CoR-UTV can exploit modern computational platforms and, consequently, can be optimized for maximum efficiency. CoR-UTV is simple and accurate, and outperforms reported alternative methods in terms of efficiency and accuracy. Simulations with synthetic data as well as real data in image reconstruction and robust principal component analysis applications support our claims.

Includes members:
  Member "high dimensional low rank plus sparse matrix decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evolutionary self expressive models for subspace clustering")
  Member "branch and bound precoding for multiuser mimo systems with 1 bit quantization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "widely linear precoding for large scale mimo with iqi algorithms and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "coherence pursuit fast simple and robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse transform autoencoder for anomaly detection")
  Member "practical sketching algorithms for low rank matrix approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation")
  Member "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "robust adaptive beamforming based on low rank and cross correlation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "fast randomized singular value thresholding for low rank optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "study of compressed randomized utv decompositions for low rank matrix approximations in data science")

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)