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: "a well tempered landscape for non convex robust subspace recovery"
  Abstract: We present a mathematical analysis of a non-convex energy landscape for Robust Subspace Recovery. We prove that an underlying subspace is the only stationary point and local minimizer in a large neighborhood if a generic condition holds for a dataset. We further show that if the generic condition is satisfied, a geodesic gradient descent method over the Grassmannian manifold can exactly recover the underlying subspace with proper initialization. The condition is shown to hold with high probability for a certain model of data.

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"
    - "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-3 (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-4 (induced by source paper "robust subspace recovery with adversarial outliers"):
  with members:
    - "an overview of 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"
    - "robust computation of linear models by convex relaxation"
    - "optimal algorithms for l_ 1 subspace signal processing"
    - "algorithms and hardness for robust subspace recovery"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-5 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"

Connected through hyperedge HE-6 (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"
    - "coherence pursuit fast simple and robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "dual principal component pursuit probability analysis and efficient algorithms"):
  with members:
    - "nonconvex robust low rank matrix recovery"
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "thresholding based efficient outlier robust pca"
    - "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-8 (induced by source paper "fast robust and non convex subspace recovery"):
  with members:
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "input sparsity and hardness for robust subspace approximation"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "complete dictionary recovery over the sphere"
    - "robust computation of linear models by convex relaxation"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "understanding alternating minimization for matrix completion"
    - "algorithms and hardness for 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.LG(Machine Learning)