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 computation of linear models by convex relaxation"
  Abstract: Consider a data set of vector-valued observations that consists of noisy inliers, which are explained well by a low-dimensional subspace, along with some number of outliers. This work describes a convex optimization problem, called reaper, that can reliably fit a low-dimensional model to this type of data. This approach parameterizes linear subspaces using orthogonal projectors and uses a relaxation of the set of orthogonal projectors to reach the convex formulation. The paper provides an efficient algorithm for solving the reaper problem, and it documents numerical experiments that confirm that reaper can dependably find linear structure in synthetic and natural data. In addition, when the inliers lie near a low-dimensional subspace, there is a rigorous theory that describes when reaper can approximate this subspace.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "consensus based optimization on the sphere ii convergence to global minimizers and machine learning"):
  with members:
    - "bad global minima exist and sgd can reach them"
    - "fast robust and non convex subspace recovery"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval stability and recovery guarantees"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-3 (induced by source paper "fair principal component analysis and filter design"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "efficient fair principal component analysis"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "global optimality in low rank matrix optimization"

Connected through hyperedge HE-4 (induced by source paper "robust subspace recovery layer for unsupervised anomaly detection"):
  with members:
    - "robust subspace recovery with adversarial outliers"
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "clustering and unsupervised anomaly detection with l2 normalized deep auto encoder representations"
    - "deep anomaly detection using geometric transformations"
    - "robust pca for anomaly detection in cyber networks"
    - "static and dynamic robust pca and matrix completion a review"
    - "robust deep and inductive anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "learning competitive and discriminative reconstructions for anomaly detection"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"
    - "deep structured energy based models for anomaly detection"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "dual principal component pursuit"
    - "very deep convolutional networks for large scale image recognition"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "robust subspace recovery with adversarial outliers"):
  with members:
    - "an overview of robust subspace recovery"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "provable self representation based outlier detection in a union of subspaces"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "input sparsity and hardness for robust subspace approximation"
    - "optimal algorithms for l_ 1 subspace signal processing"
    - "algorithms and hardness for robust subspace recovery"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-7 (induced by source paper "binary component decomposition part ii the asymmetric case"):
  with members:
    - "binary component decomposition part i the positive semidefinite case"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "convex relaxations of structured matrix factorizations"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "an overview of robust subspace recovery"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "static and dynamic robust pca via low rank sparse matrix decomposition a review"
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"
    - "static and dynamic robust pca and matrix completion a review"
    - "being robust in high dimensions can be practical"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "a well tempered landscape for non convex robust subspace recovery"

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)