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: "provable self representation based outlier detection in a union of subspaces"
  Abstract: Many computer vision tasks involve processing large amounts of data contaminated by outliers, which need to be detected and rejected. While outlier detection methods based on robust statistics have existed for decades, only recently have methods based on sparse and low-rank representation been developed along with guarantees of correct outlier detection when the inliers lie in one or more low-dimensional subspaces. This paper proposes a new outlier detection method that combines tools from sparse representation with random walks on a graph. By exploiting the property that data points can be expressed as sparse linear combinations of each other, we obtain an asymmetric affinity matrix among data points, which we use to construct a weighted directed graph. By defining a suitable Markov Chain from this graph, we establish a connection between inliers/outliers and essential/inessential states of the Markov chain, which allows us to detect outliers by using random walks. We provide a theoretical analysis that justifies the correctness of our method under geometric and connectivity assumptions. Experimental results on image databases demonstrate its superiority with respect to state-of-the-art sparse and low-rank outlier detection methods.

Connected through hyperedge HE-1 (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-2 (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"
    - "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-3 (induced by source paper "basis pursuit and orthogonal matching pursuit for subspace preserving recovery theoretical analysis"):
  with members:
    - "noisy subspace clustering via matching pursuits"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "sparse modeling for image and vision processing"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "greedy feature selection for subspace clustering"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"

Connected through hyperedge HE-4 (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"
    - "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 "robust one class kernel spectral regression"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "one class kernel spectral regression for outlier detection"
    - "one class classification taxonomy of study and review of techniques"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-6 (induced by source paper "scaled simplex representation for subspace clustering"):
  with members:
    - "ell_0 motivated low rank sparse subspace clustering"
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "discriminative regression machine a classifier for high dimensional data or imbalanced data"
    - "clustering with similarity preserving"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "discriminative transformation learning for fuzzy sparse subspace clustering"
    - "external prior guided internal prior learning for real world noisy image denoising"

Connected through hyperedge HE-7 (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"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "avid adversarial visual irregularity detection"):
  with members:
    - "a self training method for semi supervised gans"
    - "adversarially learned one class classifier for novelty detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "approximation and convergence properties of generative adversarial learning"
    - "future frame prediction for anomaly detection a new baseline"
    - "image denoising via cnns an adversarial approach"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

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.CV(Computer Vision and Pattern Recognition)