human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "linked component analysis from matrices to high order tensors applications to biomedical data"
  Abstract: With the increasing availability of various sensor technologies, we now have access to large amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be jointly analyzed to explore their latent connections. Various component analysis methods have played an increasingly important role for the analysis of such coupled data. In this article, we first provide a brief review of existing matrix-based (two-way) component analysis methods for the joint analysis of such data with a focus on biomedical applications. Then, we discuss their important extensions and generalization to multiblock multiway (tensor) data. We show how constrained multiblock tensor decomposition methods are able to extract similar or statistically dependent common features that are shared by all blocks, by incorporating the multiway nature of data. Special emphasis is given to the flexible common and individual feature analysis of multiblock data with the aim to simultaneously extract common and individual latent components with desired properties and types of diversity. Illustrative examples are given to demonstrate their effectiveness for biomedical data analysis.

Connected through hyperedge HE-1 (induced by source paper "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"):
  with members:
    - "group component analysis for multiblock data common and individual feature extraction"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"

Connected through hyperedge HE-2 (induced by source paper "online multilinear dictionary learning for sequential compressive sensing"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-3 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"

Connected through hyperedge HE-4 (induced by source paper "tensor ring decomposition"):
  with members:
    - "efficient tensor completion low rank tensor train"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "tensorizing neural networks"
    - "matrix product state for feature extraction of higher order tensors"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"

Connected through hyperedge HE-5 (induced by source paper "group component analysis for multiblock data common and individual feature extraction"):
  with members:
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "efficient nonnegative tucker decompositions algorithms and uniqueness"):
  with members:
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "decomposition of big tensors with low multilinear rank"
    - "accelerated canonical polyadic decomposition using mode reduction"
    - "mach fast randomized tensor decompositions"
    - "nonnegative approximations of nonnegative tensors"
    - "non negative sparse coding"

Question: Please tell me which class the center node 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.CE(Computational Engineering, Finance, and Science)