human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "compressive pca on graphs"):
  Abstract: Randomized algorithms reduce the complexity of low-rank recovery methods only w.r.t dimension p of a big dataset Y ∈ p×n. However, the case of large n is cumbersome to tackle without sacrificing the recovery. The recently introduced Fast Robust PCA on Graphs (FRPCAG) approximates a recovery method for matrices which are low-rank on graphs constructed between their rows and columns. In this paper we provide a novel framework, Compressive PCA on Graphs (CPCA) for an approximate recovery of such data matrices from sampled measurements. We introduce a RIP condition for low-rank matrices on graphs which enables efficient sampling of the rows and columns to perform FRPCAG on the sampled matrix. Several efficient, parallel and parameter-free decoders are presented along with their theoretical analysis for the low-rank recovery and clustering applications of PCA. On a single core machine, CPCA gains a speed up of p/k over FRPCAG, where k p is the sub-space dimension. Numerically, CPCA can efficiently cluster 70,000 MNIST digits in less than a minute and recover a low-rank matrix of size 10304×1000 in 15 secs, which is 6 and 100 times faster than FRPCAG and exact recovery.

Includes members:
  Member "high dimensional low rank plus sparse matrix decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evolutionary self expressive models for subspace clustering")
  Member "stationary signal processing on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting graph signals using kernel regression where the input signal is agnostic to a graph")
  Member "fast randomized singular value thresholding for nuclear norm minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications")
  Member "identifying outliers in large matrices via randomized adaptive compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks")
  Member "robust principal component analysis on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wasserstein based graph alignment")
  Member "random sampling of bandlimited signals on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "gspbox a toolbox for signal processing on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graphbgs background subtraction via recovery of graph signals")
  Member "unlocbox a matlab convex optimization toolbox using proximal splitting methods":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stationary signal processing on graphs")

Question: Please tell me which class the source paper 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)