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 "low rank tensor completion for multiway visual data"):
  Abstract: Tensor completion recovers missing entries of multiway data. Teh missing of entries could often be caused during teh data acquisition and transformation. In dis paper, we provide an overview of recent development in low rank tensor completion for estimating teh missing components of visual data, e. g. , color images and videos. First, we categorize these methods into two groups based on teh different optimization models. One optimizes factors of tensor decompositions wif predefined tensor rank. Teh other iteratively updates teh estimated tensor via minimizing teh tensor rank. Besides, we summarize teh corresponding algorithms to solve those optimization problems in details. Numerical experiments are given to demonstrate teh performance comparison when different methods are applied to color image and video processing.

Includes members:
  Member "tractable and scalable schatten quasi norm approximations for rank minimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization")
  Member "simultaneous tensor completion and denoising by noise inequality constrained convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "manifold modeling in embedded space a perspective for interpreting deep image prior")
  Member "missing slice recovery for tensors using a low rank model in embedded space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "block hankel tensor arima for multiple short time series forecasting")
  Member "efficient tensor completion for color image and video recovery low rank tensor train":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "efficient low rank tensor ring completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low rank tensor grid for image completion")
  Member "low rank tensor completion by truncated nuclear norm regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "truncated nuclear norm regularization for low rank tensor completion")
  Member "low tubal rank tensor completion using alternating minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on deterministic sampling patterns for robust low rank matrix completion")
  Member "scalable algorithms for tractable schatten quasi norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with deterministic sampling theories and methods")

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)