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: "coherence pursuit fast simple and robust principal component analysis"
  Abstract: This paper presents a remarkably simple, yet powerful, algorithm termed coherence pursuit (CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points. By contrast, outliers either do not admit low-dimensional structures or form small clusters. In either case, an outlier is unlikely to bear strong resemblance to a large number of data points. Given that, CoP sets an outlier apart from an inlier by comparing their coherence with the rest of the data points. The mutual coherences are computed by forming the Gram matrix of the normalized data points. Subsequently, the sought subspace is recovered from the span of the subset of the data points that exhibit strong coherence with the rest of the data. As CoP only involves one simple matrix multiplication, it is significantly faster than the state-of-the-art robust PCA algorithms. We derive analytical performance guarantees for CoP under different models for the distributions of inliers and outliers in both noise-free and noisy settings. CoP is the first robust PCA algorithm that is simultaneously non-iterative, provably robust to both unstructured and structured outliers, and can tolerate a large number of unstructured outliers.

Connected through hyperedge HE-1 (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-2 (induced by source paper "study of compressed randomized utv decompositions for low rank matrix approximations in data science"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "fast randomized singular value thresholding for low rank optimization"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-3 (induced by source paper "learning competitive and discriminative reconstructions for anomaly detection"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "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"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "a bias trick for centered robust principal component analysis"):
  with members:
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "adversarially learned one class classifier for novelty detection"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "image denoising via cnns an adversarial approach"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "compressed randomized utv decompositions for low rank approximations and big data applications"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "branch and bound precoding for multiuser mimo systems with 1 bit quantization"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "practical sketching algorithms for low rank matrix approximation"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "fast randomized singular value thresholding for low rank optimization"
    - "adaptive reduced rank receive processing based on minimum symbol error rate criterion for large scale multiple antenna systems"

Connected through hyperedge HE-8 (induced by source paper "generative probabilistic novelty detection with adversarial autoencoders"):
  with members:
    - "adversarially learned one class classifier for novelty detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "abnormal event detection in videos using generative adversarial nets"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "provable self representation based outlier detection in a union of subspaces"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"

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)