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 deterministic leverage score sampling"
  Abstract: We explain theoretically a curious empirical phenomenon: "Approximating a matrix by deterministically selecting a subset of its columns with the corresponding largest leverage scores results in a good low-rank matrix surrogate". To obtain provable guarantees, previous work requires randomized sampling of the columns with probabilities proportional to their leverage scores. #R##N#In this work, we provide a novel theoretical analysis of deterministic leverage score sampling. We show that such deterministic sampling can be provably as accurate as its randomized counterparts, if the leverage scores follow a moderately steep power-law decay. We support this power-law assumption by providing empirical evidence that such decay laws are abundant in real-world data sets. We then demonstrate empirically the performance of deterministic leverage score sampling, which many times matches or outperforms the state-of-the-art techniques.

Connected through hyperedge HE-1 (induced by source paper "improved subsampled randomized hadamard transform for linear svm"):
  with members:
    - "faster neural network training with approximate tensor operations"
    - "fully understanding the hashing trick"
    - "recovering the optimal solution by dual random projection"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "random projections for k means clustering"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "provable filter pruning for efficient neural networks"):
  with members:
    - "revisiting hard thresholding for dnn pruning"
    - "sipping neural networks sensitivity informed provable pruning of neural networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "learning filter basis for convolutional neural network compression"
    - "the state of sparsity in deep neural networks"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"

Connected through hyperedge HE-3 (induced by source paper "nystrom landmark sampling and regularized christoffel functions"):
  with members:
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "relating leverage scores and density using regularized christoffel functions"
    - "online row sampling"
    - "efficient sampling for k determinantal point processes"
    - "column selection via adaptive sampling"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-4 (induced by source paper "de anonymization attacks on neuroimaging datasets"):
  with members:
    - "constructing compact brain connectomes for individual fingerprinting"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-5 (induced by source paper "scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs"):
  with members:
    - "active orthogonal matching pursuit for sparse subspace clustering"
    - "efficient sparse subspace clustering by nearest neighbour filtering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"

Connected through hyperedge HE-6 (induced by source paper "constructing compact brain connectomes for individual fingerprinting"):
  with members:
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-7 (induced by source paper "robust frequent directions with application in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "co occuring directions sketching for approximate matrix multiply"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "sketching as a tool for numerical linear algebra"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"

Connected through hyperedge HE-8 (induced by source paper "spatial random sampling a structure preserving data sketching tool"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "robust and scalable column row sampling from corrupted big data"
    - "column selection via adaptive sampling"
    - "greedy column subset selection for large scale data sets"
    - "optimal column based low rank matrix reconstruction"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means 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.DS(Data Structures and Algorithms)