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: "fast robust pca on graphs"
  Abstract: Mining useful clusters from high dimensional data have received significant attention of the computer vision and pattern recognition community in the recent years. Linear and nonlinear dimensionality reduction has played an important role to overcome the curse of dimensionality. However, often such methods are accompanied with three different problems: high computational complexity (usually associated with the nuclear norm minimization), nonconvexity (for matrix factorization methods), and susceptibility to gross corruptions in the data. In this paper, we propose a principal component analysis (PCA) based solution that overcomes these three issues and approximates a low-rank recovery method for high dimensional datasets. We target the low-rank recovery by enforcing two types of graph smoothness assumptions, one on the data samples and the other on the features by designing a convex optimization problem. The resulting algorithm is fast, efficient, and scalable for huge datasets with O(n log(n)) computational complexity in the number of data samples. It is also robust to gross corruptions in the dataset as well as to the model parameters. Clustering experiments on 7 benchmark datasets with different types of corruptions and background separation experiments on 3 video datasets show that our proposed model outperforms 10 state-of-the-art dimensionality reduction models. Our theoretical analysis proves that the proposed model is able to recover approximate low-rank representations with a bounded error for clusterable data.

Connected through hyperedge HE-1 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph"):
  with members:
    - "greedy sampling of graph signals"
    - "supervised linear regression for graph learning from graph signals"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "graph sampling for covariance estimation"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "kernel based reconstruction of space time functions on dynamic graphs"
    - "network topology inference from spectral templates"

Connected through hyperedge HE-2 (induced by source paper "covering vectors by spaces in perturbed graphic matroids and their duals"):
  with members:
    - "sparse recovery over graph incidence matrices"
    - "covering vectors by spaces regular matroids"
    - "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
    - "fixed parameter approximability of boolean mincsps"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "nonlinear dimensionality reduction for discriminative analytics of multiple datasets"):
  with members:
    - "l1 norm principal component analysis of complex data"
    - "canonical correlation analysis of datasets with a common source graph"
    - "dpca dimensionality reduction for discriminative analytics of multiple large scale datasets"

Connected through hyperedge HE-4 (induced by source paper "depth restoration a fast low rank matrix completion via dual graph regularization"):
  with members:
    - "depth image inpainting improving low rank matrix completion with low gradient regularization"

Connected through hyperedge HE-5 (induced by source paper "spectral domain sampling of graph signals"):
  with members:
    - "blind identification of graph filters"
    - "spectral projector based graph fourier transforms"
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "stationary signal processing on graphs"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "subgraph based filterbanks for graph signals"

Connected through hyperedge HE-6 (induced by source paper "knowledge based distant regularization in learning probabilistic models"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "answering visual relational queries in web extracted knowledge graphs"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "learning the structure of generative models without labeled data"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "bayesian inference with posterior regularization and applications to infinite latent svms"

Connected through hyperedge HE-7 (induced by source paper "canonical correlation analysis of datasets with a common source graph"):
  with members:
    - "nonlinear dimensionality reduction for discriminative analytics of multiple datasets"
    - "nonlinear dimensionality reduction on graphs"
    - "dpca dimensionality reduction for discriminative analytics of multiple large scale datasets"
    - "psse redux convex relaxation decentralized robust and dynamic approaches"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "blind identification of simo wiener systems based on kernel canonical correlation analysis"

Connected through hyperedge HE-8 (induced by source paper "nonlinear dimensionality reduction on graphs"):
  with members:
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

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)