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 recovery from errors and erasures"
  Abstract: This paper considers the recovery of a low-rank matrix from an observed version that simultaneously contains both (a) erasures: most entries are not observed, and (b) errors: values at a constant fraction of (unknown) locations are arbitrarily corrupted. We provide a new unified performance guarantee on when the natural convex relaxation of minimizing rank plus support succeeds in exact recovery. Our result allows for the simultaneous presence of random and deterministic components in both the error and erasure patterns. On the one hand, corollaries obtained by specializing this one single result in different ways recover (up to poly-log factors) all the existing works in matrix completion, and sparse and low-rank matrix recovery. On the other hand, our results also provide the first guarantees for (a) recovery when we observe a vanishing fraction of entries of a corrupted matrix, and (b) deterministic matrix completion.

Connected through hyperedge HE-1 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-2 (induced by source paper "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "semi blind source separation via sparse representations and online dictionary learning"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "stable principal component pursuit"

Connected through hyperedge HE-3 (induced by source paper "a dictionary based generalization of robust pca part i study of theoretical properties"):
  with members:
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "stable principal component pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "convex graph invariant relaxations for graph edit distance"):
  with members:
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "side information for face completion a robust pca approach"):
  with members:
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "arcface additive angular margin loss for deep face recognition"
    - "a fast and accurate system for face detection identification and verification"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-6 (induced by source paper "on deterministic sampling patterns for robust low rank matrix completion"):
  with members:
    - "fundamental conditions for low cp rank tensor completion"
    - "deterministic and probabilistic conditions for finite completability of low rank multi view data"
    - "efficient low rank tensor ring completion"
    - "rank determination for low rank data completion"
    - "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"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-7 (induced by source paper "nonconvex low rank matrix recovery with arbitrary outliers via median truncated gradient descent"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-8 (induced by source paper "target based hyperspectral demixing via generalized robust pca"):
  with members:
    - "a dictionary based generalization of robust pca"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "compressive principal component pursuit"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

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)