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: "a practical randomized cp tensor decomposition"
  Abstract: The CANDECOMP/PARAFAC (CP) decomposition is a leading method for the analysis of multiway data. The standard alternating least squares algorithm for the CP decomposition (CP-ALS) involves a series ...

Connected through hyperedge HE-1 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite"):
  with members:
    - "load balanced sparse mttkrp on gpus"
    - "tensor train decomposition on tensorflow t3f"
    - "software for sparse tensor decomposition on emerging computing architectures"
    - "spartan scalable parafac2 for large sparse data"
    - "tensor regression meets gaussian processes"
    - "tensor computation a new framework for high dimensional problems in eda"
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "learning from multiway data simple and efficient tensor regression"

Connected through hyperedge HE-2 (induced by source paper "robust deep networks with randomized tensor regression layers"):
  with members:
    - "randomized tensor ring decomposition and its application to large scale data reconstruction"
    - "regularization for deep learning a taxonomy"
    - "on the implicit bias of dropout"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "tensor regression networks"
    - "tensor regression networks with various low rank tensor approximations"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "tensor contraction layers for parsimonious deep nets"

Connected through hyperedge HE-3 (induced by source paper "fast randomized matrix and tensor interpolative decomposition using countsketch"):
  with members:
    - "sketching for kronecker product regression and p splines"
    - "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data"
    - "optimal cur matrix decompositions"
    - "low rank approximation and regression in input sparsity time"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "compressed matrix multiplication"
    - "cur from a sparse optimization viewpoint"

Connected through hyperedge HE-4 (induced by source paper "low rank tucker approximation of a tensor from streaming data"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "tthresh tensor compression for multidimensional visual data"
    - "sketching for kronecker product regression and p splines"
    - "practical sketching algorithms for low rank matrix approximation"
    - "parallel tensor compression for large scale scientific data"
    - "decomposition of big tensors with low multilinear rank"
    - "tensor decompositions a new concept in brain data analysis"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-5 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data"):
  with members:
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "stable robust and super fast reconstruction of tensors using multi way projections"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "low rank approximation and regression in input sparsity time"
    - "learning topic models going beyond svd"
    - "exact recovery of sparsely used dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-6 (induced by source paper "faster johnson lindenstrauss transforms via kronecker products"):
  with members:
    - "oblivious sketching of high degree polynomial kernels"
    - "sketching for kronecker product regression and p splines"
    - "optimality of the johnson lindenstrauss lemma"
    - "the restricted isometry property of subsampled fourier matrices"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-7 (induced by source paper "randomized functional sparse tucker tensor for compression and fast visualization of scientific data"):
  with members:
    - "tuckermpi a parallel c mpi software package for large scale data compression via the tucker tensor decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "optimization with sparsity inducing penalties"
    - "faster least squares approximation"

Connected through hyperedge HE-8 (induced by source paper "randomized tensor ring decomposition and its application to large scale data reconstruction"):
  with members:
    - "randomized cp tensor decomposition"
    - "tensor ring decomposition"
    - "era of big data processing a new approach via tensor networks and tensor decompositions"
    - "decomposition of big tensors with low multilinear rank"

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.NA(Numerical Analysis)