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: "uniqueness of low rank matrix completion by rigidity theory"
  Abstract: The problem of completing a low-rank matrix from a subset of its entries is often encountered in the analysis of incomplete data sets exhibiting an underlying factor model with applications in collaborative filtering, computer vision and control. Most recent work had been focused on constructing efficient algorithms for exact or approximate recovery of the missing matrix entries and proving lower bounds for the number of known entries that guarantee a successful recovery with high probability. A related problem from both the mathematical and algorithmic point of view is the distance geometry problem of realizing points in a Euclidean space from a given subset of their pairwise distances. Rigidity theory answers basic questions regarding the uniqueness of the realization satisfying a given partial set of distances. We observe that basic ideas and tools of rigidity theory can be adapted to determine uniqueness of low-rank matrix completion, where inner products play the role that distances play in rigidity theory. This observation leads to an efficient randomized algorithm for testing both local and global unique completion. Crucial to our analysis is a new matrix, which we call the completion matrix, that serves as the analogue of the rigidity matrix.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "mc 2 a two phase algorithm for leveraged matrix completion"

Connected through hyperedge HE-2 (induced by source paper "active feature acquisition with supervised matrix completion"):
  with members:
    - "learning with feature evolvable streams"
    - "matrix completion with queries"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "high rank matrix completion and subspace clustering with missing data"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "distributed sequence memory of multidimensional inputs in recurrent networks"):
  with members:
    - "end to end memory networks"
    - "grid long short term memory"
    - "draw a recurrent neural network for image generation"
    - "learning to diagnose with lstm recurrent neural networks"
    - "learning representations from eeg with deep recurrent convolutional neural networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "speech recognition with deep recurrent neural networks"
    - "short term memory capacity in networks via the restricted isometry property"

Connected through hyperedge HE-5 (induced by source paper "matrix completion via max norm constrained optimization"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "learning with the weighted trace norm under arbitrary sampling distributions"
    - "concentration based guarantees for low rank matrix reconstruction"
    - "optimistic rates for learning with a smooth loss"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "collaborative filtering in a non uniform world learning with the weighted trace norm"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-6 (induced by source paper "combinatorial rigidity of incidence systems and application to dictionary learning"):
  with members:
    - "optimal decomposition and recombination of isostatic geometric constraint systems for designing layered materials"
    - "reduction of constraint systems"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "generalized principal component analysis gpca"
    - "exact recovery of sparsely used dictionaries"
    - "combinatorics and the rigidity of cad systems"

Connected through hyperedge HE-7 (induced by source paper "matrix completion with queries"):
  with members:
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "global registration of multiple point clouds using semidefinite programming"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "approximating the little grothendieck problem over the orthogonal and unitary groups"
    - "incremental computation of pseudo inverse of laplacian theory and applications"
    - "exact and stable recovery of rotations for robust synchronization"
    - "efficient rounding for the noncommutative grothendieck inequality"
    - "eigenvector synchronization graph rigidity and the molecule problem"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)