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 networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  Abstract: Modern applications in engineering and data science are increasinglybased on multidimensional data of exceedingly high volume, variety,and structural richness. However, standard machine learning algorithmstypically scale exponentially with data volume and complexityof cross-modal couplings - the so called curse of dimensionality -which is prohibitive to the analysis of large-scale, multi-modal andmulti-relational datasets. Given that such data are often efficientlyrepresented as multiway arrays or tensors, it is therefore timely andvaluable for the multidisciplinary machine learning and data analyticcommunities to review low-rank tensor decompositions and tensor networksas emerging tools for dimensionality reduction and large scaleoptimization problems. Our particular emphasis is on elucidating that,by virtue of the underlying low-rank approximations, tensor networkshave the ability to alleviate the curse of dimensionality in a numberof applied areas. In Part 1 of this monograph we provide innovativesolutions to low-rank tensor network decompositions and easy to interpretgraphical representations of the mathematical operations ontensor networks. Such a conceptual insight allows for seamless migrationof ideas from the flat-view matrices to tensor network operationsand vice versa, and provides a platform for further developments, practicalapplications, and non-Euclidean extensions. It also permits theintroduction of various tensor network operations without an explicitnotion of mathematical expressions, which may be beneficial for manyresearch communities that do not directly rely on multilinear algebra.Our focus is on the Tucker and tensor train TT decompositions andtheir extensions, and on demonstrating the ability of tensor networksto provide linearly or even super-linearly e.g., logarithmically scalablesolutions, as illustrated in detail in Part 2 of this monograph.

Includes members:
  Member "randomized single view algorithms for low rank matrix approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation")
  Member "group component analysis for multiblock data common and individual feature extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis")
  Member "convolutional rectifier networks as generalized tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tensor decompositions in deep learning")
  Member "tensor networks for latent variable analysis part i algorithms for tensor train decomposition":
    also connected through other hyperedges:
      (none)
  Member "linked component analysis from matrices to high order tensors applications to biomedical data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier")
  Member "parallel tensor compression for large scale scientific data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  Member "tensor ring decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution")
  Member "smooth parafac decomposition for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable model for tensor ring completion")

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)