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 graph learning from noisy data"
  Abstract: Learning graphs from data automatically has shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust PCA, where the quality of the graph plays a critical role. The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption, 2) improved graph construction by exploiting clean data recovered by robust PCA. Thus, it boosts the clustering, semi-supervised classification, and data recovery performance overall. Extensive experiments on image/document clustering, object recognition, image shadow removal, and video background subtraction reveal that our model outperforms the previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "outlier detection ensemble with embedded feature selection"):
  with members:
    - "similarity learning via kernel preserving embedding"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "feature selection a data perspective"
    - "sequential ensemble learning for outlier detection a bias variance perspective"

Connected through hyperedge HE-2 (induced by source paper "beyond vector spaces compact data representationas differentiable weighted graphs"):
  with members:
    - "dag gnn dag structure learning with graph neural networks"
    - "hyperbolic image embeddings"
    - "a comprehensive survey on graph neural networks"
    - "hyperbolic recommender systems"
    - "poincar e glove hyperbolic word embeddings"
    - "voxceleb2 deep speaker recognition"
    - "representation tradeoffs for hyperbolic embeddings"
    - "neural collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "multi view subspace clustering via partition fusion"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "multi view multiple clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "exploring graph learning for semi supervised classification beyond euclidean data"
    - "multiple partitions aligned clustering"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "unified spectral clustering with optimal graph"

Connected through hyperedge HE-4 (induced by source paper "clustering with similarity preserving"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "kernel spectral clustering and applications"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-5 (induced by source paper "multilayer collaborative low rank coding network for robust deep subspace discovery"):
  with members:
    - "robust subspace clustering with compressed data"
    - "joint label prediction based semi supervised adaptive concept factorization for robust data representation"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "robust triple matrix recovery based auto weighted label propagation for classification"
    - "discriminative local sparse representation by robust adaptive dictionary pair learning"
    - "adaptive structure constrained robust latent low rank coding for image recovery"
    - "subspace clustering by block diagonal representation"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "multi graph fusion for multi view spectral clustering"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "robust subspace discovery by block diagonal adaptive locality constrained representation"
    - "clustering with similarity preserving"
    - "deep sparse subspace clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-7 (induced by source paper "large scale multi view subspace clustering in linear time"):
  with members:
    - "multi view multiple clustering"
    - "spectral clustering via ensemble deep autoencoder learning sc edae"
    - "multiple partitions aligned clustering"
    - "clustering with similarity preserving"
    - "efficient solvers for sparse subspace clustering"
    - "multi view low rank sparse subspace clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"

Connected through hyperedge HE-8 (induced by source paper "multiple partitions aligned clustering"):
  with members:
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a survey on multi view clustering"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"

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)