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: "fast robust and non convex subspace recovery"
  Abstract: This work presents a fast and non-convex algorithm for robust subspace recovery. The data sets considered include inliers drawn around a low-dimensional subspace of a higher dimensional ambient space, and a possibly large portion of outliers that do not lie nearby this subspace. The proposed algorithm, which we refer to as Fast Median Subspace (FMS), is designed to robustly determine the underlying subspace of such data sets, while having lower computational complexity than existing methods. We prove convergence of the FMS iterates to a stationary point. Further, under a special model of data, FMS converges to a point which is near to the global minimum with overwhelming probability. Under this model, we show that the iteration complexity is globally bounded and locally $r$-linear. The latter theorem holds for any fixed fraction of outliers (less than 1) and any fixed positive distance between the limit point and the global minimum. Numerical experiments on synthetic and real data demonstrate its competitive speed and accuracy.

Connected through hyperedge HE-1 (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"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "robust computation of linear models by convex relaxation"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval stability and recovery guarantees"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-2 (induced by source paper "robust subspace recovery layer for unsupervised anomaly detection"):
  with members:
    - "robust subspace recovery with adversarial outliers"
    - "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"
    - "deep subspace clustering networks"

Connected through hyperedge HE-3 (induced by source paper "robust one class kernel spectral regression"):
  with members:
    - "one class kernel spectral regression for outlier detection"
    - "provable self representation based outlier detection in a union of subspaces"
    - "one class classification taxonomy of study and review of techniques"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-4 (induced by source paper "an overview of robust subspace recovery"):
  with members:
    - "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"
    - "coherence pursuit fast simple and robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "dual principal component pursuit probability analysis and efficient algorithms"):
  with members:
    - "nonconvex robust low rank matrix recovery"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "thresholding based efficient outlier robust pca"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"

Connected through hyperedge HE-6 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"

Connected through hyperedge HE-7 (induced by source paper "a well tempered landscape for non convex robust subspace recovery"):
  with members:
    - "an overview of robust subspace recovery"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "simple efficient and neural algorithms for sparse coding"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "input sparsity and hardness for robust subspace approximation"

Connected through hyperedge HE-8 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "fast algorithms for robust pca via gradient descent"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

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.LG(Machine Learning)