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: "guaranteed minimum rank approximation from linear observations by nuclear norm minimization with an ellipsoidal constraint"
  Abstract: The rank minimization problem is to find the lowest-rank matrix in a given set. Nuclear norm minimization has been proposed as an convex relaxation of rank minimization. Recht, Fazel, and Parrilo have shown that nuclear norm minimization subject to an affine constraint is equivalent to rank minimization under a certain condition given in terms of the rank-restricted isometry property. However, in the presence of measurement noise, or with only approximately low rank generative model, the appropriate constraint set is an ellipsoid rather than an affine space. There exist polynomial-time algorithms to solve the nuclear norm minimization with an ellipsoidal constraint, but no performance guarantee has been shown for these algorithms. In this paper, we derive such an explicit performance guarantee, bounding the error in the approximate solution provided by nuclear norm minimization with an ellipsoidal constraint.

Connected through hyperedge HE-1 (induced by source paper "run procrustes run on the convergence of accelerated procrustes flow"):
  with members:
    - "theoretical properties of the global optimizer of two layer neural network"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "spurious local minima are common in two layer relu neural networks"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "guaranteed matrix completion via non convex factorization"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "provable inductive matrix completion"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "sufficient conditions for low rank matrix recovery translated from sparse signal recovery"):
  with members:
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "nuclear norm minimization for the planted clique and biclique problems"
    - "guaranteed rank minimization via singular value projection"
    - "new bounds for restricted isometry constants"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "guaranteed rank minimization via singular value projection"):
  with members:
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "a regularity lemma and low weight approximators for low degree polynomial threshold functions"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "iterative hard thresholding for compressed sensing"

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)