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: "higher order partial least squares hopls a generalized multilinear regression method"
  Abstract: A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.

Connected through hyperedge HE-1 (induced by source paper "multilinear compressive learning"):
  with members:
    - "compressed domain image classification using a multi rate neural network"
    - "compressively sensed image recognition"
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "tensor train recurrent neural networks for video classification"
    - "multilinear class specific discriminant analysis"
    - "cryptodl deep neural networks over encrypted data"
    - "tensor representation in high frequency financial data for price change prediction"
    - "temporal attention augmented bilinear network for financial time series data analysis"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data"):
  with members:
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "smooth parafac decomposition for tensor completion"
    - "efficient nonnegative tucker decompositions algorithms and uniqueness"
    - "robust principal component analysis on graphs"
    - "bayesian sparse tucker models for dimension reduction and tensor completion"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"
    - "tensor networks for big data analytics and large scale optimization problems"

Connected through hyperedge HE-4 (induced by source paper "bayesian robust tensor factorization for incomplete multiway data"):
  with members:
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "infinite tucker decomposition nonparametric bayesian models for multiway data analysis"
    - "tensor rank some lower and upper bounds"
    - "robust pca via outlier pursuit"
    - "most tensor problems are np hard"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "a review of heterogeneous data mining for brain disorders"):
  with members:
    - "multi view machines"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "mining brain networks using multiple side views for neurological disorder identification"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
    - "a survey on multi view learning"
    - "discriminative feature selection for uncertain graph classification"

Connected through hyperedge HE-6 (induced by source paper "tensor decompositions for signal processing applications from two way to multiway component analysis"):
  with members:
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "dintucker scaling up gaussian process models on multidimensional arrays with billions of elements"
    - "low complexity damped gauss newton algorithms for candecomp parafac"
    - "nonnegative approximations of nonnegative tensors"

Connected through hyperedge HE-7 (induced by source paper "bayesian sparse tucker models for dimension reduction and tensor completion"):
  with members:
    - "bayesian cp factorization of incomplete tensors with automatic rank determination"

Connected through hyperedge HE-8 (induced by source paper "era of big data processing a new approach via tensor networks and tensor decompositions"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "overview of constrained parafac models"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

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.AI(Artificial Intelligence)