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: "nonnegative approximations of nonnegative tensors"
  Abstract: We study the decomposition of a nonnegative tensor into a minimal sum of outer product of nonnegative vectors and the associated parsimonious naive Bayes probabilistic model. We show that the corresponding approximation problem, which is central to nonnegative PARAFAC, will always have optimal solutions. The result holds for any choice of norms and, under a mild assumption, even Bregman divergences.

Connected through hyperedge HE-1 (induced by source paper "completing a joint pmf from projections a low rank coupled tensor factorization approach"):
  with members:
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-2 (induced by source paper "error preserving correction for cpd and bounded norm cpd"):
  with members:
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

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 "efficient nonnegative tucker decompositions algorithms and uniqueness"):
  with members:
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "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"
    - "non negative sparse coding"

Connected through hyperedge HE-5 (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"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "dintucker scaling up gaussian process models on multidimensional arrays with billions of elements"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

Connected through hyperedge HE-6 (induced by source paper "uniqueness of nonnegative tensor approximations"):
  with members:
    - "rank 1 tensor approximation methods and application to deflation"
    - "computing a nonnegative matrix factorization provably"
    - "most tensor problems are np hard"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "blind multilinear identification"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "most tensor problems are np hard"
    - "exact matrix completion via convex optimization"

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)