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: "subspace evolution and transfer set for low rank matrix completion"
  Abstract: We describe a new algorithm, termed subspace evolution and transfer (SET), for solving consistent low-rank matrix completion problems. The algorithm takes as its input a subset of entries of a low-rank matrix and outputs one low-rank matrix consistent with the given observations. The completion task is accomplished by searching for a column space in the Grassmann manifold that matches the incomplete observations. The SET algorithm consists of two parts-subspace evolution and subspace transfer. In the evolution part, we use a gradient descent method on the Grassmann manifold to refine our estimate of the column space. Since the gradient descent algorithm is not guaranteed to converge due to the existence of barriers along the search path, we design a new mechanism for detecting barriers and transferring the estimated column space across the barriers. This mechanism constitutes the core of the transfer step of the algorithm. The SET algorithm exhibits excellent empirical performance for a large range of sampling rates.

Connected through hyperedge HE-1 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"

Connected through hyperedge HE-2 (induced by source paper "low rank positive semidefinite matrix recovery from corrupted rank one measurements"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "compressive principal component pursuit"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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 "low rank modeling and its applications in image analysis"):
  with members:
    - "near optimal joint object matching via convex relaxation"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "compressive principal component pursuit"
    - "a riemannian geometry for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"

Connected through hyperedge HE-5 (induced by source paper "fixed rank matrix factorizations and riemannian low rank optimization"):
  with members:
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a riemannian geometry for low rank matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "regression on fixed rank positive semidefinite matrices a riemannian approach"
    - "a geometric approach to low rank matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "online robust subspace tracking from partial information"):
  with members:
    - "online identification and tracking of subspaces from highly incomplete information"
    - "high dimensional matched subspace detection when data are missing"
    - "matrix completion from noisy entries"
    - "robust principal component analysis"
    - "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.IT(Information Theory)