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: "robust principal component analysis exact recovery of corrupted low rank matrices"
  Abstract: This paper has been withdrawn due to a critical error near equation (71). This error causes the entire argument of the paper to collapse. #R##N#Emmanuel Candes of Stanford discovered the error, and has suggested a correct analysis, which will be reported in a separate publication.

Connected through hyperedge HE-1 (induced by source paper "anomaly detecting and ranking of the cloud computing platform by multi view learning"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"
    - "beyond low rank representations orthogonal clustering basis reconstruction with optimized graph structure for multi view spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "bounded manifold completion"):
  with members:
    - "network topology mapping from partial virtual coordinates and graph geodesics"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "subspace orbit randomized decomposition for low rank matrix approximations"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "fast randomized singular value thresholding for low rank optimization"
    - "robust subspace clustering"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "optimal shrinkage of singular values under random data contamination"):
  with members:
    - "randomized dimensionality reduction for k means clustering"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "a tensor based method for missing traffic data completion"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "the power of convex relaxation near optimal matrix completion"
    - "uniqueness of low rank matrix completion by rigidity theory"
    - "set an algorithm for consistent matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-6 (induced by source paper "augmented ell_1 and nuclear norm models with a globally linearly convergent algorithm"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "strongly convex programming for exact matrix completion and robust principal component analysis"
    - "a probabilistic and ripless theory of compressed sensing"
    - "guaranteed rank minimization via singular value projection"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "low rank structure learning via nonconvex heuristic recovery"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "stable principal component pursuit"
    - "dense error correction for low rank matrices via principal component pursuit"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "video de fencing"):
  with members:
    - "tilt transform invariant low rank textures"

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)