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: "group component analysis for multiblock data common and individual feature extraction"
  Abstract: Real-world data are often acquired as a collection of matrices rather than as a single matrix. Such multiblock data are naturally linked and typically share some common features while at the same time exhibiting their own individual features, reflecting the underlying data generation mechanisms. To exploit the linked nature of data, we propose a new framework for common and individual feature extraction (CIFE) which identifies and separates the common and individual features from the multiblock data. Two efficient algorithms termed common orthogonal basis extraction (COBE) are proposed to extract common basis is shared by all data, independent on whether the number of common components is known beforehand. Feature extraction is then performed on the common and individual subspaces separately, by incorporating dimensionality reduction and blind source separation techniques. Comprehensive experimental results on both the synthetic and real-world data demonstrate significant advantages of the proposed CIFE method in comparison with the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis"):
  with members:
    - "skrgan sketching rendering unconditional generative adversarial networks for medical image synthesis"
    - "generative adversarial network in medical imaging a review"
    - "brain tumor segmentation on mri with missing modalities"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "generative adversarial training for mra image synthesis using multi contrast mri"
    - "image synthesis in multi contrast mri with conditional generative adversarial networks"
    - "towards adversarial retinal image synthesis"

Connected through hyperedge HE-2 (induced by source paper "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier"):
  with members:
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"

Connected through hyperedge HE-3 (induced by source paper "model inconsistent but correlated noise multi view subspace learning with regularized mixture of gaussians"):
  with members:
    - "multi view intact space learning"
    - "overlapping trace norms for multi view learning"
    - "multi view learning in the presence of view disagreement"
    - "statistical exponential families a digest with flash cards"

Connected through hyperedge HE-4 (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"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "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-5 (induced by source paper "multi tensor completion for estimating missing values in video data"):
  with members:
    - "parallel matrix factorization for low rank tensor completion"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"

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.CV(Computer Vision and Pattern Recognition)