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: "the algebraic combinatorial approach for low rank matrix completion"
  Abstract: We present a novel algebraic combinatorial view on low-rank matrix completion based on studying relations between a few entries with tools from algebraic geometry and matroid theory. The intrinsic locality of the approach allows for the treatment of single entries in a closed theoretical and practical framework. More specifically, apart from introducing an algebraic combinatorial theory of low-rank matrix completion, we present probability-one algorithms to decide whether a particular entry of the matrix can be completed. We also describe methods to complete that entry from a few others, and to estimate the error which is incurred by any method completing that entry. Furthermore, we show how known results on matrix completion and their sampling assumptions can be related to our new perspective and interpreted in terms of a completability phase transition.

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 "matrix completion with deterministic sampling theories and methods"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "incoherence optimal matrix completion"
    - "robust recovery of subspace structures by low rank representation"
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-3 (induced by source paper "mixture matrix completion"):
  with members:
    - "incoherence optimal matrix completion"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "high rank matrix completion and subspace clustering with missing data"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from a few entries"

Connected through hyperedge HE-4 (induced by source paper "active feature acquisition with supervised matrix completion"):
  with members:
    - "learning with feature evolvable streams"
    - "matrix completion with queries"
    - "high rank matrix completion and subspace clustering with missing data"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "clipped matrix completion a remedy for ceiling effects"):
  with members:
    - "low rank methods in event detection"
    - "sub sampled cubic regularization for non convex optimization"
    - "recovering low rank matrices from few coefficients in any basis"
    - "matrix completion from noisy entries"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "sequential matrix completion"):
  with members:
    - "learning to optimize via information directed sampling"
    - "learning to optimize via posterior sampling"
    - "linear bandits in high dimension and recommendation systems"
    - "further optimal regret bounds for thompson sampling"
    - "adadelta an adaptive learning rate method"
    - "a contextual bandit approach to personalized news article recommendation"
    - "low rank matrix completion with noisy observations a quantitative comparison"
    - "matrix completion from a few entries"

Connected through hyperedge HE-7 (induced by source paper "deterministic and probabilistic conditions for finite completability of low rank multi view data"):
  with members:
    - "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor"
    - "deterministic and probabilistic conditions for finite completability of low rank tensor"
    - "low tubal rank tensor completion using alternating minimization"
    - "tensor completion by alternating minimization under the tensor train tt model"
    - "a survey on multi view learning"
    - "understanding alternating minimization for matrix completion"
    - "multi view learning in the presence of view disagreement"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-8 (induced by source paper "rank determination for low rank data completion"):
  with members:
    - "phase retrieval via matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

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)