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: "low rank matrix completion with adversarial missing entries"
  Abstract: We show that, so long as the number of missing entries in any row or column is bounded by a function of the dimension, rank, and incoherence of the matrix, nuclear norm minimization recovers the target matrix exactly. The range for which this guarantee holds is surprisingly large--in an $n\times n$ matrix of constant rank, there may be as many as $\Omega(n)$ entries missing in every row and column. Conversely, if only constantly many entries are missing in any row or column, then we may recover matrices of rank $\Omega(n)$. #R##N#We also use adversarial matrix completion to give an algorithm for completing an order-$m$ symmetric low-rank tensor from its multilinear entries in time roughly proportional to the number of tensor entries. We apply our tensor completion algorithm to the problem of learning mixtures of product distributions over the hypercube, obtaining new algorithmic results. If the centers of the product distribution are linearly independent, then we recover distributions with as many as $\Omega(n)$ centers in polynomial time and sample complexity. In the general case, we recover distributions with as many as $\tilde\Omega(n)$ centers in quasi-polynomial time, answering an open problem of Feldman et al. (SIAM J. Comp.) for the special case of distributions with incoherent bias vectors.

Connected through hyperedge HE-1 (induced by source paper "low rank approximation from communication complexity"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "parameterized low rank binary matrix approximation"
    - "relative error tensor low rank approximation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "low rank passthrough neural networks"
    - "greedy bi criteria approximations for k medians and k means"
    - "computing approximate psd factorizations"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-2 (induced by source paper "on the connection between learning two layers neural networks and tensor decomposition"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the power of sum of squares for detecting hidden structures"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "polynomial time tensor decompositions with sum of squares"

Connected through hyperedge HE-3 (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"
    - "decomposing overcomplete 3rd order tensors using sum of squares algorithms"
    - "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)