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 statistical model for tensor pca"
  Abstract: We consider the Principal Component Analysis problem for large tensors of arbitrary order $k$ under a single-spike (or rank-one plus noise) model. On the one hand, we use information theory, and recent results in probability theory, to establish necessary and sufficient conditions under which the principal component can be estimated using unbounded computational resources. It turns out that this is possible as soon as the signal-to-noise ratio $\beta$ becomes larger than $C\sqrt{k\log k}$ (and in particular $\beta$ can remain bounded as the problem dimensions increase). #R##N#On the other hand, we analyze several polynomial-time estimation algorithms, based on tensor unfolding, power iteration and message passing ideas from graphical models. We show that, unless the signal-to-noise ratio diverges in the system dimensions, none of these approaches succeeds. This is possibly related to a fundamental limitation of computationally tractable estimators for this problem. #R##N#We discuss various initializations for tensor power iteration, and show that a tractable initialization based on the spectrum of the matricized tensor outperforms significantly baseline methods, statistically and computationally. Finally, we consider the case in which additional side information is available about the unknown signal. We characterize the amount of side information that allows the iterative algorithms to converge to a good estimate.

Connected through hyperedge HE-1 (induced by source paper "community detection on mixture multi layer networks via regularized tensor decomposition"):
  with members:
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "clustering with multi layer graphs a spectral perspective"
    - "most tensor problems are np hard"
    - "matrix completion from a few entries"

Connected through hyperedge HE-2 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-3 (induced by source paper "tensor recovery from noisy and multi level quantized measurements"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "distributed flexible nonlinear tensor factorization"
    - "parallel matrix factorization for low rank tensor completion"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "nuclear norm of higher order tensors"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-4 (induced by source paper "who is afraid of big bad minima analysis of gradient flow in a spiked matrix tensor model"):
  with members:
    - "passed spurious descent algorithms and local minima in spiked matrix tensor models"
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "on the optimization landscape of tensor decompositions"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-5 (induced by source paper "mutual information for low rank even order symmetric tensor factorization"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "asymptotic mutual information for the two groups stochastic block model"

Connected through hyperedge HE-6 (induced by source paper "passed spurious analysing descent algorithms and local minima in spiked matrix tensor model"):
  with members:
    - "marvels and pitfalls of the langevin algorithm in noisy high dimensional inference"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "depth creates no bad local minima"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "a tight degree 4 sum of squares lower bound for the sherrington kirkpatrick hamiltonian"):
  with members:
    - "computational hardness of certifying bounds on constrained pca problems"
    - "bayesian estimation from few samples community detection and related problems"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "sum of squares proofs and the quest toward optimal algorithms"

Connected through hyperedge HE-8 (induced by source paper "algorithms for an efficient tensor biclustering"):
  with members:
    - "tensor principal component analysis via sum of squares proofs"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

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.LG(Machine Learning)