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 "efficient nonnegative tucker decompositions algorithms and uniqueness"):
  Abstract: Nonnegative Tucker decomposition (NTD) is a powerful tool for the extraction of nonnegative parts-based and physically meaningful latent components from high-dimensional tensor data while preserving the natural multilinear structure of data. However, as the data tensor often has multiple modes and is large scale, the existing NTD algorithms suffer from a very high computational complexity in terms of both storage and computation time, which has been one major obstacle for practical applications of NTD. To overcome these disadvantages, we show how low (multilinear) rank approximation (LRA) of tensors is able to significantly simplify the computation of the gradients of the cost function, upon which a family of efficient first-order NTD algorithms are developed. Besides dramatically reducing the storage complexity and running time, the new algorithms are quite flexible and robust to noise, because any well-established LRA approaches can be applied. We also show how nonnegativity incorporating sparsity substantially improves the uniqueness property and partially alleviates the curse of dimensionality of the Tucker decompositions. Simulation results on synthetic and real-world data justify the validity and high efficiency of the proposed NTD algorithms.

Includes members:
  Member "linked component analysis from matrices to high order tensors applications to biomedical data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jointly learning structured analysis discriminative dictionary and analysis multiclass classifier")
  Member "bayesian sparse tucker models for dimension reduction and tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tensor computation a new framework for high dimensional problems in eda")
  Member "decomposition of big tensors with low multilinear rank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pasta a parallel sparse tensor algorithm benchmark suite")
  Member "accelerated canonical polyadic decomposition using mode reduction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linked component analysis from matrices to high order tensors applications to biomedical data")
  Member "mach fast randomized tensor decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust deep networks with randomized tensor regression layers")
  Member "nonnegative approximations of nonnegative tensors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "completing a joint pmf from projections a low rank coupled tensor factorization approach")
  Member "non negative sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed bayesian matrix decomposition for big data mining and clustering")

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.LG(Machine Learning)