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 pca via outlier pursuit"
  Abstract: Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, well-documented sensitivity to outliers. Recent work has considered the setting where each point has a few arbitrarily corrupted components. Yet, in applications of SVD or PCA such as robust collaborative filtering or bioinformatics, malicious agents, defective genes, or simply corrupted or contaminated experiments may effectively yield entire points that are completely corrupted. #R##N#We present an efficient convex optimization-based algorithm we call Outlier Pursuit, that under some mild assumptions on the uncorrupted points (satisfied, e.g., by the standard generative assumption in PCA problems) recovers the exact optimal low-dimensional subspace, and identifies the corrupted points. Such identification of corrupted points that do not conform to the low-dimensional approximation, is of paramount interest in bioinformatics and financial applications, and beyond. Our techniques involve matrix decomposition using nuclear norm minimization, however, our results, setup, and approach, necessarily differ considerably from the existing line of work in matrix completion and matrix decomposition, since we develop an approach to recover the correct column space of the uncorrupted matrix, rather than the exact matrix itself. In any problem where one seeks to recover a structure rather than the exact initial matrices, techniques developed thus far relying on certificates of optimality, will fail. We present an important extension of these methods, that allows the treatment of such problems.

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 "arae adversarially robust training of autoencoders improves novelty detection"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "rare event detection using disentangled representation learning"
    - "real world anomaly detection in surveillance videos"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-3 (induced by source paper "correlation aware deep generative model for unsupervised anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "adversarially learned anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "deep structured energy based models for anomaly detection"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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 "inverse transform autoencoder for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"

Connected through hyperedge HE-6 (induced by source paper "matrix completion with deterministic sampling theories and methods"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "incoherence optimal matrix completion"
    - "robust recovery of subspace structures by low rank representation"
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-7 (induced by source paper "optimal analysis of subset selection based l_p low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "towards a zero one law for entrywise low rank approximation"
    - "relative error tensor low rank approximation"
    - "how to escape saddle points efficiently"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank approximation of binary matrices column subset selection and generalizations"
    - "optimal cur matrix decompositions"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-8 (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"
    - "robust computation of linear models by convex relaxation"
    - "very deep convolutional networks for large scale image recognition"

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)