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: "exact tensor completion with sum of squares"
  Abstract: We obtain the first polynomial-time algorithm for exact tensor completion that improves over the bound implied by reduction to matrix completion. The algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed entries of the tensor. This bound improves over the previous best one of $r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound also matches the best known results for the easier problem of approximate tensor completion (Barak & Moitra, 2015). #R##N#Our algorithm and analysis extends seminal results for exact matrix completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares method. The main technical challenge is to show that a small number of randomly chosen monomials are enough to construct a degree-3 polynomial with precisely planted orthogonal global optima over the sphere and that this fact can be certified within the sum-of-squares proof system.

Connected through hyperedge HE-1 (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"
    - "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"
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-2 (induced by source paper "sum of squares meets square loss fast rates for agnostic tensor completion"):
  with members:
    - "efficient algorithms for outlier robust regression"
    - "sum of squares lower bounds for refuting any csp"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "complexity theoretic limitations on learning halfspaces"
    - "learning without concentration"
    - "nuclear norm of higher order tensors"
    - "a new convex relaxation for tensor completion"

Connected through hyperedge HE-3 (induced by source paper "new dependencies of hierarchies in polynomial optimization"):
  with members:
    - "optimization over the boolean hypercube via sums of nonnegative circuit polynomials"
    - "on the bit complexity of sum of squares proofs"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "the power of sherali adams relaxations for general valued csps"
    - "a 1 epsilon approximation for makespan scheduling with precedence constraints using lp hierarchies"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "lower bounds on the size of semidefinite programming relaxations"

Connected through hyperedge HE-4 (induced by source paper "iterative collaborative filtering for sparse noisy tensor estimation"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "spectral algorithms for tensor completion"
    - "noisy tensor completion via the sum of squares hierarchy"
    - "nuclear norm of higher order tensors"
    - "understanding alternating minimization for matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-5 (induced by source paper "spectral methods from tensor networks"):
  with members:
    - "message passing algorithms for synchronization problems over compact groups"
    - "estimation in the group action channel"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"
    - "the sample complexity of multi reference alignment"
    - "sample complexity of the boolean multireference alignment problem"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"

Connected through hyperedge HE-6 (induced by source paper "recovery guarantees for quadratic tensors with limited observations"):
  with members:
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "an overview of embedding models of entities and relationships for knowledge base completion"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "recovery guarantee of non negative matrix factorization via alternating updates"
    - "riemannian preconditioning for tensor completion"
    - "efficient estimation of word representations in vector space"
    - "tensor decompositions for learning latent variable models"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-7 (induced by source paper "efficient algorithms for outlier robust regression"):
  with members:
    - "learning geometric concepts with nasty noise"
    - "better agnostic clustering via relaxed tensor norms"
    - "mixture models robustness and sum of squares proofs"
    - "learning from untrusted data"
    - "robust estimators in high dimensions without the computational intractability"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "robust regression and lasso"

Connected through hyperedge HE-8 (induced by source paper "mixture models robustness and sum of squares proofs"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "learning geometric concepts with nasty noise"
    - "outlier robust moment estimation via sum of squares"
    - "being robust in high dimensions can be practical"
    - "on the bit complexity of sum of squares proofs"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"
    - "the power of sum of squares for detecting hidden structures"

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)