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: "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
  Abstract: Tensor rank and low-rank tensor decompositions have many applications in learning and complexity theory. Most known algorithms use unfoldings of tensors and can only handle rank up to $n^{\lfloor p/2 \rfloor}$ for a $p$-th order tensor in $\mathbb{R}^{n^p}$. Previously no efficient algorithm can decompose 3rd order tensors when the rank is super-linear in the dimension. Using ideas from sum-of-squares hierarchy, we give the first quasi-polynomial time algorithm that can decompose a random 3rd order tensor decomposition when the rank is as large as $n^{3/2}/\textrm{polylog} n$. #R##N#We also give a polynomial time algorithm for certifying the injective norm of random low rank tensors. Our tensor decomposition algorithm exploits the relationship between injective norm and the tensor components. The proof relies on interesting tools for decoupling random variables to prove better matrix concentration bounds, which can be useful in other settings.

Connected through hyperedge HE-1 (induced by source paper "learning two layer rectified neural networks in polynomial time"):
  with members:
    - "complexity of training relu neural networks"
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "learning one convolutional layer with overlapping patches"
    - "the computational complexity of training relu s"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"
    - "learning two layer neural networks with symmetric inputs"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"

Connected through hyperedge HE-2 (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"
    - "exact tensor completion with sum of squares"
    - "sample complexity of the boolean multireference alignment problem"
    - "a nearly tight sum of squares lower bound for the planted clique problem"

Connected through hyperedge HE-3 (induced by source paper "smoothed analysis of discrete tensor decomposition and assemblies of neurons"):
  with members:
    - "polynomial time tensor decompositions with sum of squares"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-4 (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"
    - "exact tensor completion with sum of squares"
    - "better agnostic clustering via relaxed tensor norms"
    - "robustly learning a gaussian getting optimal error efficiently"

Connected through hyperedge HE-5 (induced by source paper "the power of sum of squares for detecting hidden structures"):
  with members:
    - "exact tensor completion with sum of squares"
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "polynomial time tensor decompositions with sum of squares"
    - "multiplicative approximations for polynomial optimization over the unit sphere"
    - "how well do local algorithms solve semidefinite programs"
    - "spectral algorithms for tensor completion"
    - "strongly refuting random csps below the spectral threshold"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"

Connected through hyperedge HE-6 (induced by source paper "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"):
  with members:
    - "a practical randomized cp tensor decomposition"
    - "learning from multiway data simple and efficient tensor regression"
    - "polynomial time tensor decompositions with sum of squares"
    - "reinforcement learning of pomdps using spectral methods"
    - "tensorly tensor learning in python"
    - "tensor factorization via matrix factorization"
    - "learning mixtures of gaussians in high dimensions"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "on the optimization landscape of tensor decompositions"):
  with members:
    - "polynomial time tensor decompositions with sum of squares"
    - "matrix completion has no spurious local minimum"
    - "gradient descent learns linear dynamical systems"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "the loss surfaces of multilayer networks"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "smoothed analysis of tensor decompositions"

Connected through hyperedge HE-8 (induced by source paper "polynomial time tensor decompositions with sum of squares"):
  with members:
    - "a nearly tight sum of squares lower bound for the planted clique problem"
    - "learning mixtures of gaussians in high dimensions"
    - "low rank matrix completion with adversarial missing entries"
    - "fast spectral algorithms from sum of squares proofs tensor decomposition and planted sparse vectors"
    - "lower bounds on the size of semidefinite programming relaxations"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "a tensor approach to learning mixed membership community models"
    - "smoothed analysis of tensor decompositions"

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.DS(Data Structures and Algorithms)